diff --git a/CHANGELOG.md b/CHANGELOG.md
index 66d04fb..040beb2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # @vladmandic/face-api  
 
-Version: **1.1.9**  
+Version: **1.1.10**  
 Description: **FaceAPI: AI-powered Face Detection, Description & Recognition using Tensorflow/JS**  
 
 Author: **Vladimir Mandic <mandic00@live.com>**  
@@ -9,8 +9,14 @@ Repository: **<git+https://github.com/vladmandic/face-api.git>**
 
 ## Changelog
 
-### **HEAD -> master** 2021/04/03 mandic00@live.com
+### **HEAD -> master** 2021/04/06 mandic00@live.com
 
+- fixed bug which led to latency not being measured and wrong output on console for demo
+- add cdn links
+
+### **1.1.10** 2021/04/04 mandic00@live.com
+
+- added webhints
 
 ### **1.1.9** 2021/04/03 mandic00@live.com
 
diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js
index c6f860f..ad9aa18 100644
--- a/dist/face-api.esm-nobundle.js
+++ b/dist/face-api.esm-nobundle.js
@@ -2080,7 +2080,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) {
 }
 
 // package.json
-var version = "1.1.9";
+var version = "1.1.10";
 
 // src/xception/extractParams.ts
 function extractorsFactory2(extractWeights, paramMappings) {
diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index f594986..ec9c105 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -76642,7 +76642,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) {
 }
 
 // package.json
-var version10 = "1.1.9";
+var version10 = "1.1.10";
 
 // src/xception/extractParams.ts
 function extractorsFactory2(extractWeights, paramMappings) {
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index 0a00d11..db5fa9e 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,6 +1,6 @@
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   "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename 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Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw 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ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"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\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var 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\"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var 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_Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");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;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(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;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}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);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}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);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({ x, y, width, height });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<TSource>(sourceObj: TSource, detection: FaceDetection): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) return resolve(null);\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(inputs: Array<TResolvedNetInput>, treatAsBatchInput: boolean = false) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) return inputs;\n  const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n  if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n  const inputArray = inputArgArray.map(resolveInput);\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n    }\n  });\n  // wait for all media elements being loaded\n  await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n  let canvas = input as HTMLCanvasElement;\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n    if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n  }\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections\n    .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height });\n    if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections\n      .map((det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det))\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = t.dataSync();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<TSource>(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks,\n    unshiftedLandmarks: TFaceLandmarks,\n    alignedRect: FaceDetection,\n    angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n  // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n  const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n  // convert radians to degrees\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n\n  const angle = { roll: <number | undefined>undefined, pitch: <number | undefined>undefined, yaw: <number | undefined>undefined };\n\n  if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n  const pt = mesh._positions;\n\n  // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n  // value of 0 means center\n\n  // roll is face lean from left to right\n  // comparing x,y of outside corners of leftEye and rightEye\n  angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n  // pitch is face turn from left right\n  // comparing x distance of top of nose to left and right edge of face\n  // precision is lacking since coordinates are not precise enough\n  angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n  // yaw is face move from up to down\n  // comparing size of the box around the face with top and bottom of detected landmarks\n  // silly hack, but this gives us face compression on y-axis\n  // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n  const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n  const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n  angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n  return angle;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n  const angle = calculateFaceAngle(unshiftedLandmarks);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n    angle,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (ageTensor.dataSync())[0];\n        const probMale = (genderTensor.dataSync())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(landmarkTensor.dataSync());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    /*\n    if (x.shape[1] === 512 && x.shape[3] === 3) {\n      console.log('Input:', x.shape, x.size, 'sum:', x.reshape([786432]).sum().dataSync()[0]); // input does not change (checked values)\n      console.log('Filter:', params.filters.shape, params.filters.size, 'sum:', params.filters.reshape([864]).sum().dataSync()[0]); // params do not change (checked values)\n      console.log('Strides', strides);\n      console.log('Conv2d 1st 5 values:', out.shape, out.size, out.dataSync().slice(0, 5)); // output has different values!\n      console.log('Conv2D sum of all values:', tf.reshape(out, [2097152]).sum().dataSync()[0]); // silly sum just to see how much results diverged\n    }\n    */\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) conv11 = out;\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) return 0.0;\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(maxOutputSize, numBoxes);\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], 2)),\n    tf.add(vec[1], tf.div(sizes[1], 2)),\n  ];\n  return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return { boxes: boxesByBatch, scores: scoresByBatch };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n    if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n      const features = mobileNetV1(x, params.mobilenetv1);\n      const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n    const netInput = await toNetInput(input);\n    const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n    const scoresData = Array.from(scores.dataSync());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(left, top, right - left, bottom - top),\n          { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n        );\n      });\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(255) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  // eslint-disable-next-line no-unused-vars\n  public async then(onfulfilled: (value: T) => T | PromiseLike<T>): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n  const results = await computeResults(faces);\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<TSource extends WithFaceDetection<{}>> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(\n        faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>),\n      ),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<TSource extends WithFaceDetection<{}>> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<TSource extends WithFaceDetection<{}>> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>)),\n      this.extractedFaces,\n    );\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<TSource extends WithFaceDetection<{}>> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) return undefined;\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<TSource extends WithFaceDetection<{}>> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n    const faceLandmarksByFace = await Promise.all(\n      faces.map((face) => this.landmarkNet.detectLandmarks(face)),\n    ) as FaceLandmarks68[];\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<TSource extends WithFaceDetection<{}>> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  // eslint-disable-next-line no-unused-vars\n  constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n    let result;\n    if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n    else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n    else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n    else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n    return result;\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>((resolve, reject) => {\n      this.run()\n        .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n        .catch((err) => reject(err));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n"],
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   "names": []
diff --git a/dist/face-api.js b/dist/face-api.js
index 6bf98d2..0a6dcc8 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -76768,7 +76768,7 @@ return a / b;`;
   }
 
   // package.json
-  var version10 = "1.1.9";
+  var version10 = "1.1.10";
 
   // src/xception/extractParams.ts
   function extractorsFactory2(extractWeights, paramMappings) {
diff --git a/dist/face-api.js.map b/dist/face-api.js.map
index 912e8cc..a4f9850 100644
--- a/dist/face-api.js.map
+++ b/dist/face-api.js.map
@@ -1,6 +1,6 @@
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   "sourcesContent": ["import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename 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Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw 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ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"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\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var 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\"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var 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_Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");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;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(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;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}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);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}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);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false,\n) {\n  ctx.beginPath();\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx];\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(x, y);\n  });\n\n  if (isClosed) {\n    const from = points[points.length - 1];\n    const to = points[0];\n    if (!from || !to) {\n      return;\n    }\n\n    ctx.moveTo(from.x, from.y);\n    ctx.lineTo(to.x, to.y);\n  }\n\n  ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0;\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = 10 ** prec;\n  return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width);\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n  private _width: number\n\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n    }\n\n    this._width = width;\n    this._height = height;\n  }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height);\n  }\n}\n", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x;\n    this._y = y;\n  }\n\n  get x(): number { return this._x; }\n\n  get y(): number { return this._y; }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y);\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y);\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y);\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y);\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y));\n  }\n\n  public magnitude(): number {\n    return Math.sqrt((this.x ** 2) + (this.y ** 2));\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y));\n  }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n    }\n  }\n\n  private _x: number\n\n  private _y: number\n\n  private _width: number\n\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any;\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n    Box.assertIsValidBox({\n      x, y, width, height,\n    }, 'Box.constructor', allowNegativeDimensions);\n\n    this._x = x;\n    this._y = y;\n    this._width = width;\n    this._height = height;\n  }\n\n  public get x(): number { return this._x; }\n\n  public get y(): number { return this._y; }\n\n  public get width(): number { return this._width; }\n\n  public get height(): number { return this._height; }\n\n  public get left(): number { return this.x; }\n\n  public get top(): number { return this.y; }\n\n  public get right(): number { return this.x + this.width; }\n\n  public get bottom(): number { return this.y + this.height; }\n\n  public get area(): number { return this.width * this.height; }\n\n  public get topLeft(): Point { return new Point(this.left, this.top); }\n\n  public get topRight(): Point { return new Point(this.right, this.top); }\n\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n  public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.round(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map((val) => Math.floor(val));\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public toSquare(): Box<BoxType> {\n    let {\n      x, y, width, height,\n    } = this;\n    const diff = Math.abs(width - height);\n    if (width < height) {\n      x -= (diff / 2);\n      width += diff;\n    }\n    if (height < width) {\n      y -= (diff / 2);\n      height += diff;\n    }\n\n    return new Box({ x, y, width, height });\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY,\n    });\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    const [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY,\n    ];\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this;\n    const clippedX = Math.max(x, 0);\n    const clippedY = Math.max(y, 0);\n\n    const newWidth = right - clippedX;\n    const newHeight = bottom - clippedY;\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n    return (new Box({\n      x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n    })).floor();\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this;\n    const x = this.x + sx;\n    const y = this.y + sy;\n\n    return new Box({\n      x, y, width, height,\n    });\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1;\n    const h = this.height + 1;\n\n    const dx = 1;\n    const dy = 1;\n    let edx = w;\n    let edy = h;\n\n    let x = this.left;\n    let y = this.top;\n    let ex = this.right;\n    let ey = this.bottom;\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w;\n      ex = imageWidth;\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h;\n      ey = imageHeight;\n    }\n    if (x < 1) {\n      edy = 2 - x;\n      x = 1;\n    }\n    if (y < 1) {\n      edy = 2 - y;\n      y = 1;\n    }\n\n    return {\n      dy, edy, dx, edx, y, ey, x, ex, w, h,\n    };\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height),\n    }).toSquare().round();\n  }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({\n      left, top, right, bottom,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n\n  private _classScore: number\n\n  private _className: string\n\n  private _box: Rect\n\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions,\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n    this._score = score;\n    this._classScore = classScore;\n    this._className = className;\n    this._box = new Box(relativeBox).rescale(this._imageDims);\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n\n  public get className(): string { return this._className; }\n\n  public get box(): Box { return this._box; }\n\n  public get imageDims(): Dimensions { return this._imageDims; }\n\n  public get imageWidth(): number { return this.imageDims.width; }\n\n  public get imageHeight(): number { return this.imageDims.height; }\n\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height },\n    );\n  }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions,\n  ) {\n    super(score, score, '', relativeBox, imageDims);\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height);\n    return new FaceDetection(score, relativeBox, imageDims);\n  }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n  const interSection = width * height;\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map((pt) => pt.x);\n  const ys = pts.map((pt) => pt.y);\n  const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n  const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n  const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n  const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n  return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true,\n): number[] {\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map((c) => c.boxIndex);\n\n  const pick: number[] = [];\n\n  while (indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number;\n    pick.push(curr);\n\n    const indices = indicesSortedByScore;\n\n    const outputs: number[] = [];\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i];\n\n      const currBox = boxes[curr];\n      const idxBox = boxes[idx];\n\n      outputs.push(iou(currBox, idxBox, isIOU));\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold,\n    );\n  }\n\n  return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb;\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n    return tf.sub(x, avg_rgb);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [height, width] = imgTensor.shape.slice(1);\n    if (height === width) {\n      return imgTensor;\n    }\n\n    const dimDiff = Math.abs(height - width);\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n    const paddingAxis = height > width ? 2 : 1;\n\n    const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice();\n      paddingTensorShape[paddingAxis] = paddingAmountLocal;\n      return tf.fill(paddingTensorShape, 0, 'float32');\n    };\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount);\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null;\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend,\n    ]\n      .filter((t) => !!t)\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[];\n    return tf.concat(tensorsToStack, paddingAxis);\n  });\n}\n", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice();\n  for (let i = array.length - 1; i > 0; i--) {\n    const j = Math.floor(Math.random() * (i + 1));\n    const x = array[i];\n    array[i] = array[j];\n    array[j] = x;\n  }\n  return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({\n      x, y, width, height,\n    }, allowNegativeDimensions);\n  }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n\n  protected _positions: Point[]\n\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0),\n  ) {\n    const { width, height } = imgDims;\n    this._imgDims = new Dimensions(width, height);\n    this._shift = shift;\n    this._positions = relativeFaceLandmarkPositions.map(\n      (pt) => pt.mul(new Point(width, height)).add(shift),\n    );\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n  public get imageWidth(): number { return this._imgDims.width; }\n\n  public get imageHeight(): number { return this._imgDims.height; }\n\n  public get positions(): Point[] { return this._positions; }\n\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n    );\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height },\n    );\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y),\n    );\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y);\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection);\n\n      return this.shiftBy(box.x, box.y).align(null, options);\n    }\n\n    const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n    if (useDlibAlignment) {\n      return this.alignDlib();\n    }\n\n    return this.alignMinBbox(minBoxPadding);\n  }\n\n  private alignDlib(): Box {\n    const centers = this.getRefPointsForAlignment();\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n    const size = Math.floor(eyeToMouthDist / relScale);\n\n    const refPoint = getCenterPoint(centers);\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions);\n    return box.pad(box.width * padding, box.height * padding);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class');\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions;\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]]),\n    ];\n  }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17);\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22);\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27);\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36);\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42);\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48);\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68);\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth(),\n    ].map(getCenterPoint);\n  }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label;\n    this._distance = distance;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get distance(): number { return this._distance; }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n  }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee);\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box);\n    this._label = label;\n  }\n\n  public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n  private _label: string\n\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n    }\n\n    this._label = label;\n    this._descriptors = descriptors;\n  }\n\n  public get label(): string { return this._label; }\n\n  public get descriptors(): Float32Array[] { return this._descriptors; }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d)),\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee);\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n    }\n  }\n\n  private _score: number\n\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label);\n    this._score = score;\n    this._classScore = classScore;\n  }\n\n  public get score(): number { return this._score; }\n\n  public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection<TSource>(sourceObj: TSource, detection: FaceDetection): WithFaceDetection<TSource> {\n  const extension = { detection };\n  return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n  const fetch = window.fetch;\n  if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n  const readFile = () => {\n    throw new Error('readFile - filesystem not available for browser environment');\n  };\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile,\n  };\n}\n", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n  let requireFsError = '';\n\n  if (!fs) {\n    try {\n      // eslint-disable-next-line global-require\n      fs = require('fs');\n    } catch (err) {\n      requireFsError = err.toString();\n    }\n  }\n\n  const readFile = fs\n    ? (filePath: string) => new Promise<Buffer>((resolve, reject) => {\n      fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n    })\n    : () => {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n    };\n\n  return {\n    readFile,\n  };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n  // eslint-disable-next-line dot-notation\n  const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n  const Image = global.Image || global.HTMLImageElement;\n\n  const createCanvasElement = () => {\n    if (Canvas) return new Canvas();\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n  };\n\n  const createImageElement = () => {\n    if (Image) return new Image();\n    throw new Error('createImageElement - missing Image implementation for nodejs environment');\n  };\n\n  const fetch = global.fetch;\n  // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n  const fileSystem = createFileSystem();\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n    Image: Image || class {},\n    ImageData: global.ImageData || class {},\n    Video: global.HTMLVideoElement || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem,\n  };\n}\n", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    && typeof process !== 'undefined' && !!process.version;\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n  }\n  return environment;\n}\n\nfunction setEnv(env: Environment) {\n  environment = env;\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) return setEnv(createBrowserEnv());\n  if (isNodejs()) return setEnv(createNodejsEnv());\n  return null;\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize();\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env;\n  environment.Canvas = Canvas;\n  environment.Image = Image;\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n  environment.createImageElement = env.createImageElement || (() => new Image());\n\n  environment.ImageData = env.ImageData || environment.ImageData;\n  environment.Video = env.Video || environment.Video;\n  environment.fetch = env.fetch || environment.fetch;\n  environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg);\n  }\n  return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg;\n  }\n\n  const canvas = resolveInput(canvasArg);\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n  }\n\n  const ctx = canvas.getContext('2d');\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null');\n  }\n\n  return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n  // eslint-disable-next-line no-unused-vars\n  TOP_LEFT = 'TOP_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  TOP_RIGHT = 'TOP_RIGHT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  // eslint-disable-next-line no-unused-vars\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n\n  public backgroundColor: string\n\n  public fontColor: string\n\n  public fontSize: number\n\n  public fontStyle: string\n\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const {\n      anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = options;\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n    this.fontSize = fontSize || 14;\n    this.fontStyle = fontStyle || 'Georgia';\n    this.padding = padding || 4;\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n\n  public anchor : IPoint\n\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {},\n  ) {\n    // eslint-disable-next-line no-nested-ternary\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text);\n    this.anchor = anchor;\n    this.options = new DrawTextFieldOptions(options);\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options;\n    return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options;\n    return this.text.length * fontSize + (2 * padding);\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options;\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n    const textFieldWidth = this.measureWidth(ctx);\n    const textFieldHeight = this.measureHeight();\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims;\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n      return { x: newX, y: newY };\n    }\n    return { x, y };\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg);\n    const ctx = getContext2dOrThrow(canvas);\n\n    const {\n      backgroundColor, fontColor, fontSize, fontStyle, padding,\n    } = this.options;\n\n    ctx.font = `${fontSize}px ${fontStyle}`;\n    const maxTextWidth = this.measureWidth(ctx);\n    const textHeight = this.measureHeight();\n\n    ctx.fillStyle = backgroundColor;\n    const upperLeft = this.getUpperLeft(ctx, canvas);\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x;\n      const y = padding + upperLeft.y + ((i + 1) * fontSize);\n      ctx.fillText(textLine, x, y);\n    });\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n\n  public lineWidth: number\n\n  public drawLabelOptions: DrawTextFieldOptions\n\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const {\n      boxColor, lineWidth, label, drawLabelOptions,\n    } = options;\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n    this.lineWidth = lineWidth || 2;\n    this.label = label;\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor,\n    };\n    this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {},\n  ) {\n    this.box = new Box(box);\n    this.options = new DrawBoxOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const { boxColor, lineWidth } = this.options;\n\n    const {\n      x, y, width, height,\n    } = this.box;\n    ctx.strokeStyle = boxColor;\n    ctx.lineWidth = lineWidth;\n    ctx.strokeRect(x, y, width, height);\n\n    const { label } = this.options;\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n    }\n  }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>,\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n  detectionsArray.forEach((det) => {\n    // eslint-disable-next-line no-nested-ternary\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n    // eslint-disable-next-line no-nested-ternary\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n    const label = score ? `${round(score)}` : undefined;\n    new DrawBox(box, { label }).draw(canvasArg);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n  const { Image, Video } = env.getEnv();\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n  // eslint-disable-next-line consistent-return\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) return resolve(null);\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return;\n      // eslint-disable-next-line no-use-before-define\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      reject(e);\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return;\n      e.currentTarget.removeEventListener('load', onLoad);\n      e.currentTarget.removeEventListener('error', onError);\n      resolve(e);\n    }\n\n    media.addEventListener('load', onLoad);\n    media.addEventListener('error', onError);\n  });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n    const reader = new FileReader();\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n      const img = env.getEnv().createImageElement();\n      img.onload = () => resolve(img);\n      img.onerror = reject;\n      img.src = reader.result as string;\n    };\n    reader.onerror = reject;\n    reader.readAsDataURL(buf);\n  });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n  const { Image, Video } = env.getEnv();\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight);\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight);\n  }\n  return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n  const { createCanvasElement } = env.getEnv();\n  const canvas = createCanvasElement();\n  canvas.width = width;\n  canvas.height = height;\n  return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n  const { ImageData } = env.getEnv();\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet');\n  }\n\n  const { width, height } = dims || getMediaDimensions(media);\n  const canvas = createCanvas({ width, height });\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n  }\n  return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement,\n): Promise<HTMLCanvasElement> {\n  const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n  await tf.browser.toPixels(imgTensor3D, targetCanvas);\n\n  imgTensor3D.dispose();\n\n  return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n  const { Image, Canvas, Video } = env.getEnv();\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n  const { Image, Canvas } = env.getEnv();\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n  }\n\n  if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n  const dims = getMediaDimensions(input);\n  const scale = inputSize / Math.max(dims.height, dims.width);\n  const width = scale * dims.width;\n  const height = scale * dims.height;\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n  const offset = Math.abs(width - height) / 2;\n  const dx = centerImage && width < height ? offset : 0;\n  const dy = centerImage && height < width ? offset : 0;\n  if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n  return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n\n  private _canvases: HTMLCanvasElement[] = []\n\n  private _batchSize: number\n\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n\n  private _inputSize: number\n\n  constructor(inputs: Array<TResolvedNetInput>, treatAsBatchInput: boolean = false) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput;\n    this._batchSize = inputs.length;\n\n    inputs.forEach((input, idx) => {\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = input.shape;\n        return;\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0];\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n        }\n\n        this._imageTensors[idx] = input;\n        this._inputDimensions[idx] = (input as any).shape.slice(1);\n        return;\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n      this._canvases[idx] = canvas;\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n    });\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors;\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases;\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput;\n  }\n\n  public get batchSize(): number {\n    return this._batchSize;\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions;\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize;\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n    );\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx];\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0];\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1];\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n    }\n\n    const width = this.getInputWidth(batchIdx);\n    const height = this.getInputHeight(batchIdx);\n    return computeReshapedDimensions({ width, height }, this.inputSize);\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n    this._inputSize = inputSize;\n\n    return tf.tidy(() => {\n      const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n        const input = this.getInput(batchIdx);\n\n        if (input instanceof tf.Tensor) {\n          let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n          imgTensor = padToSquare(imgTensor, isCenterInputs);\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false);\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3);\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n      });\n\n      const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n\n      return batchTensor;\n    });\n  }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) return inputs;\n  const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n  if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n  const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n  const inputArray = inputArgArray.map(resolveInput);\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n      if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n    }\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0];\n      if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n    }\n  });\n  // wait for all media elements being loaded\n  await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n  return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]> {\n  const { Canvas } = env.getEnv();\n  let canvas = input as HTMLCanvasElement;\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input);\n    if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n    const tensorOrCanvas = netInput.getInput(0);\n    canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n  }\n  const ctx = getContext2dOrThrow(canvas);\n  const boxes = detections\n    .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n    .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height });\n    if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n    return faceImg;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor3D[]> {\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported');\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n\n    const boxes = detections\n      .map((det) => (det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det))\n      .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n    const faceTensors = boxes.map(({\n      x, y, width, height,\n    }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\n    return faceTensors;\n  });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n  url: string,\n  // eslint-disable-next-line no-undef\n  init?: RequestInit,\n): Promise<Response> {\n  const { fetch } = env.getEnv();\n  const res = await fetch(url, init);\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n  }\n  return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri);\n  const blob = await (res).blob();\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n  }\n  return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename,\n    };\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`,\n    };\n  }\n  // eslint-disable-next-line no-nested-ternary\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter((s) => s);\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename;\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri);\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference;\n  input.width = width;\n  input.height = height;\n  return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork<TNetParams> {\n  constructor(name: string) {\n    this._name = name;\n  }\n\n  protected _params: TNetParams | undefined = undefined\n\n  protected _paramMappings: ParamMapping[] = []\n\n  public _name: any;\n\n  public get params(): TNetParams | undefined { return this._params; }\n\n  public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n  public get isLoaded(): boolean { return !!this.params; }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    return obj[objProp];\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath);\n    obj[objProp].dispose();\n    obj[objProp] = tensor;\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath),\n    }));\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable());\n    });\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync());\n      variable.dispose();\n      this.reassignParamFromPath(path, tensor);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach((param) => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`);\n      }\n      param.tensor.dispose();\n    });\n    this._params = undefined;\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr)),\n    );\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl);\n      return;\n    }\n    await this.loadFromUri(weightsOrUrl);\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`);\n    }\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n    }\n    const { readFile } = env.getEnv();\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\n    const manifest = JSON.parse((await readFile(manifestUri)).toString());\n    const weightMap = await loadWeights(manifest, modelBaseUri);\n    this.loadFromWeightMap(weightMap);\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const { paramMappings, params } = this.extractParams(weights);\n    this._paramMappings = paramMappings;\n    this._params = params;\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error('traversePropertyPath - model has no loaded params');\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      // eslint-disable-next-line no-prototype-builtins\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n      }\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n    }, { nextObj: this.params });\n\n    const { obj, objProp } = result;\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n    }\n\n    return { obj, objProp };\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n  // eslint-disable-next-line no-unused-vars\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number],\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n    out = tf.add(out, params.bias);\n    return out;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n  });\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias,\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n    ) as tf.Tensor4D;\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false,\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias,\n    ) as tf.Tensor4D;\n\n    return withRelu ? tf.relu(out) : out;\n  });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach((path) => {\n    if (!paramMappings.some((pm) => pm.originalPath === path)) {\n      weightMap[path].dispose();\n    }\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams => {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): FCParams => {\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias,\n    };\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  // eslint-disable-next-line no-useless-constructor\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    public depthwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public pointwise_filter: tf.Tensor4D,\n    // eslint-disable-next-line no-unused-vars\n    public bias: tf.Tensor1D,\n  // eslint-disable-next-line no-empty-function\n  ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[],\n) {\n  return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n\nexport function loadSeparableConvParamsFactory(\n  // eslint-disable-next-line no-unused-vars\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T,\n) {\n  return (prefix: string): SeparableConvParams => {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4);\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias,\n    );\n  };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  return (originalPath: string, paramRank: number, mappedPath?: string) => {\n    const tensor = weightMap[originalPath];\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath },\n    );\n\n    return tensor;\n  };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights;\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights);\n    remainingWeights = remainingWeights.slice(numWeights);\n    return ret;\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights;\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights,\n  };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock4Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: {\n      dense0, dense1, dense2, dense3,\n    },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return (prefix: string): ConvParams => {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1);\n\n    return { filters, bias };\n  };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n    return { conv0, conv1, conv2 };\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`);\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n    return {\n      conv0, conv1, conv2, conv3,\n    };\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock4Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n  constructor() {\n    super('FaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n      let out = denseBlock4(normalized, params.dense0, true);\n      out = denseBlock4(out, params.dense1);\n      out = denseBlock4(out, params.dense2);\n      out = denseBlock4(out, params.dense3);\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams,\n): tf.Tensor2D {\n  return tf.tidy(() => tf.add(\n    tf.matMul(x, params.weights),\n    params.bias,\n  ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: extractFcParams('fc'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n  const featureExtractorMap: tf.NamedTensorMap = {};\n  const classifierMap: tf.NamedTensorMap = {};\n\n  Object.keys(weightMap).forEach((key) => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n    map[key] = weightMap[key];\n  });\n\n  return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name);\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor;\n  }\n\n  protected abstract getDefaultModelName(): string\n\n  protected abstract getClassifierChannelsIn(): number\n\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n    });\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const cIn = this.getClassifierChannelsIn();\n    const cOut = this.getClassifierChannelsOut();\n    const classifierWeightSize = (cOut * cIn) + cOut;\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n  public neutral: number\n\n  public happy: number\n\n  public sad: number\n\n  public angry: number\n\n  public fearful: number\n\n  public disgusted: number\n\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx];\n    });\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map((expression) => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor);\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)));\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n      const data = t.dataSync();\n      t.dispose();\n      return data;\n    }));\n    out.dispose();\n\n    const predictionsByBatch = probabilitesByBatch\n      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7;\n  }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions<TSource>(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions<TSource> {\n  const extension = { expressions };\n  return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint,\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n  faceExpressionsArray.forEach((e) => {\n    // eslint-disable-next-line no-nested-ternary\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined);\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n    }\n\n    const sorted = expr.asSortedArray();\n    const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0));\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n      anchor,\n    );\n    drawTextField.draw(canvasArg);\n  });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 > = TSource & {\n    landmarks: TFaceLandmarks,\n    unshiftedLandmarks: TFaceLandmarks,\n    alignedRect: FaceDetection,\n    angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n  }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    // eslint-disable-next-line dot-notation\n    && obj['landmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    // eslint-disable-next-line dot-notation\n    && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n  // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n  const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n  // convert radians to degrees\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n\n  const angle = { roll: <number | undefined>undefined, pitch: <number | undefined>undefined, yaw: <number | undefined>undefined };\n\n  if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n  const pt = mesh._positions;\n\n  // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n  // value of 0 means center\n\n  // roll is face lean from left to right\n  // comparing x,y of outside corners of leftEye and rightEye\n  angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n  // pitch is face turn from left right\n  // comparing x distance of top of nose to left and right edge of face\n  // precision is lacking since coordinates are not precise enough\n  angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n  // yaw is face move from up to down\n  // comparing size of the box around the face with top and bottom of detected landmarks\n  // silly hack, but this gives us face compression on y-axis\n  // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n  const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n  const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n  angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n  return angle;\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks> {\n  const { box: shift } = sourceObj.detection;\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y);\n\n  const rect = landmarks.align();\n  const { imageDims } = sourceObj.detection;\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n  const angle = calculateFaceAngle(unshiftedLandmarks);\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect,\n    angle,\n  };\n\n  return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n\n  public drawPoints: boolean\n\n  public lineWidth: number\n\n  public pointSize: number\n\n  public lineColor: string\n\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const {\n      drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n    } = options;\n    this.drawLines = drawLines;\n    this.drawPoints = drawPoints;\n    this.lineWidth = lineWidth || 1;\n    this.pointSize = pointSize || 2;\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {},\n  ) {\n    this.faceLandmarks = faceLandmarks;\n    this.options = new DrawFaceLandmarksOptions(options);\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg);\n\n    const {\n      drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n    } = this.options;\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor;\n      ctx.lineWidth = lineWidth;\n      drawContour(ctx, this.faceLandmarks.getJawOutline());\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n      drawContour(ctx, this.faceLandmarks.getNose());\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n      drawContour(ctx, this.faceLandmarks.getMouth(), true);\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor;\n      ctx.fillStyle = pointColor;\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath();\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n        ctx.fill();\n      };\n      this.faceLandmarks.positions.forEach(drawPoint);\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>,\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n  faceLandmarksArray.forEach((f) => {\n    // eslint-disable-next-line no-nested-ternary\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg);\n  });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n    return { separable_conv0, separable_conv1, expansion_conv };\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n    return { separable_conv0, separable_conv1, separable_conv2 };\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1,\n  };\n\n  const middle_flow = {};\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n  });\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x;\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n  out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n  return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n  out = tf.add(out, x);\n  return out;\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception');\n    this._numMainBlocks = numMainBlocks;\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n    if (!params) {\n      throw new Error('TinyXception - load model before inference');\n    }\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n      out = reductionBlock(out, params.entry_flow.reduction_block_1);\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n      });\n      out = reductionBlock(out, params.exit_flow.reduction_block);\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks);\n  }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n  const age = extractFCParams(512, 1, 'fc/age');\n  const gender = extractFCParams(512, 2, 'fc/gender');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry(`${prefix}/weights`, 2);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { weights, bias };\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender'),\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n  // eslint-disable-next-line no-unused-vars\n  FEMALE = 'female',\n  // eslint-disable-next-line no-unused-vars\n  MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet');\n    this._faceFeatureExtractor = faceFeatureExtractor;\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor;\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`);\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input;\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n      const gender = fullyConnectedLayer(pooled, params.fc.gender);\n      return { age, gender };\n    });\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input);\n      return { age, gender: tf.softmax(gender) };\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput);\n\n    const ages = tf.unstack(out.age);\n    const genders = tf.unstack(out.gender);\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i],\n    }));\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (ageTensor.dataSync())[0];\n        const probMale = (genderTensor.dataSync())[0];\n        const isMale = probMale > 0.5;\n        const gender = isMale ? Gender.MALE : Gender.FEMALE;\n        const genderProbability = isMale ? probMale : (1 - probMale);\n\n        ageTensor.dispose();\n        genderTensor.dispose();\n        return { age, gender, genderProbability };\n      }),\n    );\n    out.age.dispose();\n    out.gender.dispose();\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model';\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose);\n    super.dispose(throwOnRedispose);\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights);\n    this._params = params;\n    this._paramMappings = paramMappings;\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n    return extractParamsFromWeightMap(classifierMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n    return this.extractClassifierParams(classifierWeights);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width);\n      return {\n        width: width * scale,\n        height: height * scale,\n      };\n    });\n\n    const batchSize = inputDimensions.length;\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n      // eslint-disable-next-line no-unused-vars\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx];\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n      };\n\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          getPaddingX(batchIdx),\n          getPaddingY(batchIdx),\n        ))))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n          inputDimensions[batchIdx].width,\n          inputDimensions[batchIdx].height,\n        ))));\n\n      return landmarkTensors as tf.Tensor2D;\n    });\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input);\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width })),\n      );\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input);\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput)),\n    );\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(landmarkTensor.dataSync());\n        const xCoords = landmarksArray.filter((_, i) => isEven(i));\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width: netInput.getInputWidth(batchIdx),\n          },\n        );\n      },\n    ));\n\n    landmarkTensors.forEach((t) => t.dispose());\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136;\n  }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n  weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractDenseBlock3Params,\n  } = loadParamsFactory(weightMap, paramMappings);\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2'),\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const {\n    extractDenseBlock3Params,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 },\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n  constructor() {\n    super('TinyFaceFeatureExtractor');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n      let out = denseBlock3(normalized, params.dense0, true);\n      out = denseBlock3(out, params.dense1);\n      out = denseBlock3(out, params.dense2);\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n      return out;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMapTiny(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights);\n  }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor);\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model';\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128;\n  }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n  const { filters, bias } = params.conv;\n\n  let out = tf.conv2d(x, filters, strides, padding);\n  out = tf.add(out, bias);\n  out = scale(out, params.scale);\n  return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues);\n    const depth = weights.length / (numFilters * filterSize * filterSize);\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0],\n      ),\n    );\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvParams {\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n    const bias = tf.tensor1d(extractWeights(numFilters));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n    const weights = tf.tensor1d(extractWeights(numWeights));\n    const biases = tf.tensor1d(extractWeights(numWeights));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` },\n    );\n\n    return {\n      weights,\n      biases,\n    };\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): ConvLayerParams {\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n    return { conv, scale };\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false,\n  ): ResidualLayerParams {\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n    return { conv1, conv2 };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n  );\n  paramMappings.push({ paramPath: 'fc' });\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n    const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n    const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n    return { weights, biases };\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n    const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n    const scale = extractScaleLayerParams(prefix);\n\n    return { conv: { filters, bias }, scale };\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`),\n    };\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  const conv32_down = extractConvLayerParams('conv32_down');\n  const conv32_1 = extractResidualLayerParams('conv32_1');\n  const conv32_2 = extractResidualLayerParams('conv32_2');\n  const conv32_3 = extractResidualLayerParams('conv32_3');\n\n  const conv64_down = extractResidualLayerParams('conv64_down');\n  const conv64_1 = extractResidualLayerParams('conv64_1');\n  const conv64_2 = extractResidualLayerParams('conv64_2');\n  const conv64_3 = extractResidualLayerParams('conv64_3');\n\n  const conv128_down = extractResidualLayerParams('conv128_down');\n  const conv128_1 = extractResidualLayerParams('conv128_1');\n  const conv128_2 = extractResidualLayerParams('conv128_2');\n\n  const conv256_down = extractResidualLayerParams('conv256_down');\n  const conv256_1 = extractResidualLayerParams('conv256_1');\n  const conv256_2 = extractResidualLayerParams('conv256_2');\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n  const { fc } = weightMap;\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc,\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n  out = tf.add(out, x);\n  out = tf.relu(out);\n  return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1);\n  out = convNoRelu(out, params.conv2);\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape);\n  const isPad = pooled.shape[3] !== out.shape[3];\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number];\n    padShapeX[1] = 1;\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX);\n    out = tf.concat([out, zerosW], 1);\n\n    const padShapeY = [...out.shape] as [number, number, number, number];\n    padShapeY[2] = 1;\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY);\n    out = tf.concat([out, zerosH], 2);\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n  out = tf.add(pooled, out) as tf.Tensor4D;\n\n  out = tf.relu(out);\n  return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('FaceRecognitionNet');\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298];\n      const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n      let out = convDown(normalized, params.conv32_down);\n      out = tf.maxPool(out, 3, 2, 'valid');\n\n      out = residual(out, params.conv32_1);\n      out = residual(out, params.conv32_2);\n      out = residual(out, params.conv32_3);\n\n      out = residualDown(out, params.conv64_down);\n      out = residual(out, params.conv64_1);\n      out = residual(out, params.conv64_2);\n      out = residual(out, params.conv64_3);\n\n      out = residualDown(out, params.conv128_down);\n      out = residual(out, params.conv128_1);\n      out = residual(out, params.conv128_2);\n\n      out = residualDown(out, params.conv256_down);\n      out = residual(out, params.conv256_1);\n      out = residual(out, params.conv256_2);\n      out = residualDown(out, params.conv256_down_out);\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n      const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n      return fullyConnected;\n    });\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n    const netInput = await toNetInput(input);\n    const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n    faceDescriptorTensors.forEach((t) => t.dispose());\n    return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet();\n  net.extractWeights(weights);\n  return net;\n}\n", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n>(\n  sourceObj: TSource,\n  descriptor: Float32Array,\n): WithFaceDescriptor<TSource> {\n  const extension = { descriptor };\n  return { ...sourceObj, ...extension };\n}\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n  TSource\n>(\n  sourceObj: TSource,\n  age: number,\n): WithAge<TSource> {\n  const extension = { age };\n  return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n    && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n  TSource\n>(\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number,\n): WithGender<TSource> {\n  const extension = { gender, genderProbability };\n  return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` },\n    );\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance,\n    };\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean,\n  ): ConvParams {\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut],\n    );\n    const bias = tf.tensor1d(extractWeights(channelsOut));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n    );\n\n    return { filters, bias };\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n  ): PointwiseConvParams {\n    const {\n      filters,\n      bias,\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n    return {\n      filters,\n      batch_norm_offset: bias,\n    };\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string,\n  ): MobileNetV1.ConvPairParams {\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n    return { depthwise_conv, pointwise_conv };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13,\n    };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0,\n    };\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1,\n    };\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2,\n    };\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3,\n    };\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4,\n    };\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5,\n    };\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5,\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  const mobilenetv1 = extractMobilenetV1Params();\n  const prediction_layer = extractPredictionLayerParams();\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4],\n  );\n  const output_layer = {\n    extra_dim,\n  };\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer,\n    },\n    paramMappings,\n  };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n    const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n    const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n    return { filters, batch_norm_offset };\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n    const mappedPrefix = `mobilenetv1/conv_${idx}`;\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n    const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n    const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n    const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n    const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n    const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance,\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n    };\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13),\n    };\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n    const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n    return { filters, bias };\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n    );\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`,\n    );\n    return { box_encoding_predictor, class_predictor };\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5),\n    };\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams,\n  } = extractorsFactory(weightMap, paramMappings);\n  const extra_dim = weightMap['Output/extra_dim'];\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim,\n    },\n  };\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n  return tf.tidy(() => {\n    let out = tf.conv2d(x, params.filters, strides, 'same');\n    /*\n    if (x.shape[1] === 512 && x.shape[3] === 3) {\n      console.log('Input:', x.shape, x.size, 'sum:', x.reshape([786432]).sum().dataSync()[0]); // input does not change (checked values)\n      console.log('Filter:', params.filters.shape, params.filters.size, 'sum:', params.filters.reshape([864]).sum().dataSync()[0]); // params do not change (checked values)\n      console.log('Strides', strides);\n      console.log('Conv2d 1st 5 values:', out.shape, out.size, out.dataSync().slice(0, 5)); // output has different values!\n      console.log('Conv2D sum of all values:', tf.reshape(out, [2097152]).sum().dataSync()[0]); // silly sum just to see how much results diverged\n    }\n    */\n    out = tf.add(out, params.batch_norm_offset);\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n  return tf.tidy(() => {\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon,\n    );\n    return tf.clipByValue(out, 0, 6);\n  });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13,\n    ];\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1;\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n      if (layerIdx === 11) conv11 = out;\n    });\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null');\n    }\n\n    return {\n      out,\n      conv11: conv11 as any,\n    };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync();\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) return 0.0;\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n): number[] {\n  const numBoxes = boxes.shape[0];\n  const outputSize = Math.min(maxOutputSize, numBoxes);\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter((c) => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score);\n\n  const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n  const selected: number[] = [];\n\n  candidates.forEach((c) => {\n    if (selected.length >= outputSize) return;\n    const originalScore = c.score;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j]);\n      if (iou === 0.0) continue;\n      c.score *= suppressFunc(iou);\n      if (c.score <= scoreThreshold) break;\n    }\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex);\n    }\n  });\n  return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1]),\n  ];\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], 2)),\n    tf.add(vec[1], tf.div(sizes[1], 2)),\n  ];\n  return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out),\n    ]),\n    [1, 0],\n  );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n  return tf.tidy(() => {\n    const batchSize = boxPredictions.shape[0];\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n    );\n    boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n    scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n    return { boxes: boxesByBatch, scores: scoresByBatch };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams,\n) {\n  return tf.tidy(() => {\n    const batchSize = x.shape[0];\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4],\n    );\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3],\n    );\n    return { boxPredictionEncoding, classPrediction };\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams,\n) {\n  return tf.tidy(() => {\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding,\n    ], 1) as tf.Tensor4D;\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction,\n    ], 1) as tf.Tensor4D;\n\n    return {\n      boxPredictions,\n      classPredictions,\n    };\n  });\n}\n", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5;\n    this._maxResults = maxResults || 100;\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`);\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence; }\n\n  get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n  constructor() {\n    super('SsdMobilenetv1');\n  }\n\n  public forwardInput(input: NetInput) {\n    const { params } = this;\n    if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n      const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n      const features = mobileNetV1(x, params.mobilenetv1);\n      const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n      return outputLayer(boxPredictions, classPredictions, params.output_layer);\n    });\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input));\n  }\n\n  public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise<FaceDetection[]> {\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n    const netInput = await toNetInput(input);\n    const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n    const boxes = _boxes[0];\n    const scores = _scores[0];\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose();\n      _scores[i].dispose();\n    }\n    const scoresData = Array.from(scores.dataSync());\n    const iouThreshold = 0.5;\n    const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n    const reshapedDims = netInput.getReshapedInputDimensions(0);\n    const inputSize = netInput.inputSize as number;\n    const padX = inputSize / reshapedDims.width;\n    const padY = inputSize / reshapedDims.height;\n    const boxesData = boxes.arraySync();\n    const results = indices\n      .map((idx) => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2]),\n        ].map((val) => val * padY);\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3]),\n        ].map((val) => val * padX);\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(left, top, right - left, bottom - top),\n          { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n        );\n      });\n    boxes.dispose();\n    scores.dispose();\n    return results;\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights);\n  }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1();\n  net.extractWeights(weights);\n  return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`);\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612));\n    return tf.add(tf.relu(tf.sub(x, min)), min);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n    out = tf.sub(out, params.bn.sub);\n    out = tf.mul(out, params.bn.truediv);\n    out = tf.add(out, params.conv.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n    out = tf.add(out, params.bias);\n    return leaky(out);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n    const sub = tf.tensor1d(extractWeights(size));\n    const truediv = tf.tensor1d(extractWeights(size));\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` },\n    );\n    return { sub, truediv };\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n    return { conv, bn };\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const {\n    extractWeights,\n    getRemainingWeights,\n  } = extractWeightsFactory(weights);\n\n  const paramMappings: ParamMapping[] = [];\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(extractWeights, paramMappings);\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0');\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n    params = {\n      conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n    };\n  }\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n  }\n  return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry(`${prefix}/sub`, 1);\n    const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n    return { sub, truediv };\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry(`${prefix}/filters`, 4);\n    const bias = extractWeightEntry(`${prefix}/bias`, 1);\n    return { filters, bias };\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`);\n    const bn = extractBatchNormParams(`${prefix}/bn`);\n    return { conv, bn };\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  };\n}\n\nexport function extractParamsFromWeightMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n  const paramMappings: ParamMapping[] = [];\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams,\n  } = extractorsFactory(weightMap, paramMappings);\n\n  let params: TinyYolov2NetParams;\n\n  if (config.withSeparableConvs) {\n    // eslint-disable-next-line no-mixed-operators\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8'),\n    };\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8'),\n    };\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings);\n  return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416;\n    this._scoreThreshold = scoreThreshold || 0.5;\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n    }\n  }\n\n  get inputSize(): number { return this._inputSize; }\n\n  get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n  public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2');\n    validateConfig(config);\n    this._config = config;\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config;\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1;\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0);\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n    let out = convWithBatchNorm(x, params.conv0);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = convWithBatchNorm(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = convWithBatchNorm(out, params.conv6);\n    out = convWithBatchNorm(out, params.conv7);\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv1);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv2);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv3);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv4);\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n    out = depthwiseSeparableConv(out, params.conv5);\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n    return convLayer(out, params.conv8, 'valid', false);\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n    const { params } = this;\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference');\n    }\n\n    return tf.tidy(() => {\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor;\n      batchTensor = batchTensor.div(255) as tf.Tensor4D;\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n    });\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input), inputSize);\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n    const netInput = await toNetInput(input);\n    const out = await this.forwardInput(netInput, inputSize);\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0),\n    };\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n    out.dispose();\n    out0.dispose();\n\n    const boxes = results.map((res) => res.box);\n    const scores = results.map((res) => res.score);\n    const classScores = results.map((res) => res.classScore);\n    const classNames = results.map((res) => this.config.classes[res.label]);\n\n    const indices = nonMaxSuppression(\n      boxes.map((box) => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true,\n    );\n\n    const detections = indices.map((idx) => new ObjectDetection(\n      scores[idx],\n      classScores[idx],\n      classNames[idx],\n      boxes[idx],\n      inputDimensions,\n    ));\n    return detections;\n  }\n\n  protected getDefaultModelName(): string {\n    return '';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeightMap(weightMap, this.config);\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n    const numFilters = filterSizes ? filterSizes.length : undefined;\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number,\n  ) {\n    const { width, height } = inputBlobDimensions;\n    const inputSize = Math.max(width, height);\n    const correctionFactorX = inputSize / width;\n    const correctionFactorY = inputSize / height;\n\n    const numCells = outputTensor.shape[1];\n    const numBoxes = this.config.anchors.length;\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0);\n      return [boxes, scores, classScores];\n    });\n\n    const results = [] as any;\n    const scoresData = await scoresTensor.array();\n    const boxesData = await boxesTensor.array();\n    for (let row = 0; row < numCells; row++) {\n      for (let col = 0; col < numCells; col++) {\n        for (let anchor = 0; anchor < numBoxes; anchor++) {\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n            const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n            const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n            const x = (ctX - (widthLocal / 2));\n            const y = (ctY - (heightLocal / 2));\n            const pos = { row, col, anchor };\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 };\n            results.push({\n              box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n              score,\n              classScore: score * classScore,\n              label,\n              ...pos,\n            });\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose();\n    scoresTensor.dispose();\n    classScoresTensor.dispose();\n    return results;\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n    const { row, col, anchor } = pos;\n    const classesData = await classesTensor.array();\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label,\n      }))\n      .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n  constructor(withSeparableConvs: boolean = true) {\n    const config = {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      ...(withSeparableConvs\n        ? {\n          anchors: BOX_ANCHORS_SEPARABLE,\n          meanRgb: MEAN_RGB_SEPARABLE,\n        }\n        : {\n          anchors: BOX_ANCHORS,\n          withClassScores: true,\n        }),\n    };\n\n    super(config);\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs;\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs);\n  net.extractWeights(weights);\n  return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}\n", "export class ComposableTask<T> {\n  // eslint-disable-next-line no-unused-vars\n  public async then(onfulfilled: (value: T) => T | PromiseLike<T>): Promise<T> {\n    return onfulfilled(await this.run());\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented');\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n  const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n    ? getRectForAlignment(parentResult)\n    : parentResult.detection));\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  );\n  const results = await computeResults(faces);\n  faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n  return results;\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  // eslint-disable-next-line no-unused-vars\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  // eslint-disable-next-line no-unused-vars\n  getRectForAlignment?: (parentResultLocal: WithFaceLandmarks<TSource, any>) => FaceDetection,\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async (faces) => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment,\n  );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512],\n    };\n\n    super(config);\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors;\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams);\n    return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model';\n  }\n\n  protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeightMap(weightMap);\n  }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<TSource extends WithFaceDetection<{}>> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n    const parentResults = await this.parentTask;\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(\n        faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>),\n      ),\n      this.extractedFaces,\n    );\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i]),\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<TSource extends WithFaceDetection<{}>> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      (face) => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces,\n    );\n\n    return extendWithFaceExpressions(parentResult, faceExpressions);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input);\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictAllFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictSingleFaceExpressionsTask<TSource> {\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>,\n  ) {\n    super();\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<TSource extends WithFaceDetection<{}>> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n    const parentResults = await this.parentTask;\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>)),\n      this.extractedFaces,\n    );\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i];\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n    });\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<TSource extends WithFaceDetection<{}>> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) return undefined;\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces,\n    );\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input);\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictAllAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictSingleAgeAndGenderTask<TSource> {\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n  ) {\n    super();\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>)),\n      null,\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      // eslint-disable-next-line no-shadow\n      (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n    );\n\n    return extendWithFaceDescriptor(parentResult, descriptor);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    // eslint-disable-next-line no-unused-vars\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    // eslint-disable-next-line no-unused-vars\n    protected input: TNetInput,\n    // eslint-disable-next-line no-unused-vars\n    protected useTinyLandmarkNet: boolean,\n  ) {\n    super();\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net;\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<TSource extends WithFaceDetection<{}>> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n    const parentResults = await this.parentTask;\n    const detections = parentResults.map((res) => res.detection);\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections);\n    const faceLandmarksByFace = await Promise.all(\n      faces.map((face) => this.landmarkNet.detectLandmarks(face)),\n    ) as FaceLandmarks68[];\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n    return parentResults.map((parentResult, i) => extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i]));\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input);\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<TSource extends WithFaceDetection<{}>> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n    const parentResult = await this.parentTask;\n    if (!parentResult) {\n      return undefined;\n    }\n    const { detection } = parentResult;\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection]);\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n    faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks);\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input);\n  }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  // eslint-disable-next-line no-unused-vars\n  constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n    super();\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n  public async run(): Promise<FaceDetection[]> {\n    const { input, options } = this;\n    let result;\n    if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n    else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n    else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n    else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n    return result;\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>((resolve, reject) => {\n      this.run()\n        .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n        .catch((err) => reject(err));\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n    );\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach((faceDetection) => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    // eslint-disable-next-line no-async-promise-executor\n    return new Promise<WithFaceDetection<{}> | undefined>(async (resolve) => {\n      const detection = await this.run();\n      resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n    });\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet,\n    );\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n    );\n  }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n\n  const desc1 = Array.from(arr1);\n  const desc2 = Array.from(arr2);\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + (diff ** 2), 0),\n  );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6,\n  ) {\n    this._distanceThreshold = distanceThreshold;\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n\n    if (!inputArray.length) {\n      throw new Error('FaceRecognizer.constructor - expected atleast one input');\n    }\n\n    let count = 1;\n    const createUniqueLabel = () => `person ${count++}`;\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc;\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n      }\n\n      throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>');\n    });\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n  public get distanceThreshold(): number { return this._distanceThreshold; }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map((d) => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1);\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n        label,\n        this.computeMeanDistance(queryDescriptor, descriptors),\n      ))\n      .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor);\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance);\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON()),\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector();\n  net.extractWeights(weights);\n  return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height);\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height));\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height);\n  }\n\n  return results;\n}\n"],
   "mappings": 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   "names": []
diff --git a/dist/face-api.node-cpu.js b/dist/face-api.node-cpu.js
index 7ec2af7..98b2562 100644
--- a/dist/face-api.node-cpu.js
+++ b/dist/face-api.node-cpu.js
@@ -2262,7 +2262,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) {
 }
 
 // package.json
-var version = "1.1.9";
+var version = "1.1.10";
 
 // src/ageGenderNet/AgeGenderNet.ts
 var tf20 = __toModule(require_tfjs_esm());
diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js
index 0385ed9..1c8bf0d 100644
--- a/dist/face-api.node-gpu.js
+++ b/dist/face-api.node-gpu.js
@@ -2262,7 +2262,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) {
 }
 
 // package.json
-var version = "1.1.9";
+var version = "1.1.10";
 
 // src/ageGenderNet/AgeGenderNet.ts
 var tf20 = __toModule(require_tfjs_esm());
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
index 41bd3f3..f875d30 100644
--- a/dist/face-api.node.js
+++ b/dist/face-api.node.js
@@ -2262,7 +2262,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) {
 }
 
 // package.json
-var version = "1.1.9";
+var version = "1.1.10";
 
 // src/ageGenderNet/AgeGenderNet.ts
 var tf20 = __toModule(require_tfjs_esm());
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index fcf6927..1cec0ea 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -1326,7 +1326,7 @@ var require_worker_threads = __commonJS(() => {
 var require_perf_hooks = __commonJS(() => {
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
 var require_tfjs_backend_wasm_threaded_simd = __commonJS((exports, module) => {
   var WasmBackendModuleThreadedSimd = function() {
     var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
@@ -3515,7 +3515,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS((exports, module) => {
     exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd;
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js
 var require_tfjs_backend_wasm = __commonJS((exports, module) => {
   var WasmBackendModule = function() {
     var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
@@ -4468,7 +4468,7 @@ var require_tfjs_backend_wasm = __commonJS((exports, module) => {
     exports["WasmBackendModule"] = WasmBackendModule;
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -4563,7 +4563,7 @@ function notYetImplemented(kernelName) {
   throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -5026,7 +5026,7 @@ function isPromise(object) {
   return object && object.then && typeof object.then === "function";
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/environment.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/environment.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -5162,7 +5162,7 @@ function setEnvironmentGlobal(environment) {
   ENV = environment;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -5216,7 +5216,7 @@ function getGlobal(key, init2) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
 var Abs = "Abs";
 var Acos = "Acos";
 var Acosh = "Acosh";
@@ -5376,7 +5376,7 @@ var _FusedMatMul = "_FusedMatMul";
 var FusedConv2D = "FusedConv2D";
 var FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -5459,7 +5459,7 @@ function makeKey(kernelName, backendName) {
   return `${backendName}_${kernelName}`;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/util.js
 var util_exports = {};
 __export(util_exports, {
   arraysEqual: () => arraysEqual,
@@ -5584,7 +5584,7 @@ function decodeString(bytes, encoding = "utf-8") {
   return env().platform.decode(bytes, encoding);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -5684,7 +5684,7 @@ var Logger = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tape.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tape.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -5800,7 +5800,7 @@ function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -5942,7 +5942,7 @@ function createComplexTuples(vals) {
   return complexTuples;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -6182,7 +6182,7 @@ Object.defineProperty(Variable, Symbol.hasInstance, {
   }
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
 var tensor_util_exports = {};
 __export(tensor_util_exports, {
   assertTypesMatch: () => assertTypesMatch,
@@ -6191,7 +6191,7 @@ __export(tensor_util_exports, {
   makeTypesMatch: () => makeTypesMatch
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/types.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/types.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -6265,7 +6265,7 @@ function sumOutType(type) {
   return upcastType(type, "int32");
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -6325,7 +6325,7 @@ function isIterable(obj) {
   return Array.isArray(obj) || typeof obj === "object";
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/engine.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/engine.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7080,7 +7080,7 @@ function add(a, b) {
   return ENGINE.runKernel(Add, inputs);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js
 var device_util_exports = {};
 __export(device_util_exports, {
   isBrowser: () => isBrowser,
@@ -7116,7 +7116,7 @@ function isBrowser() {
   return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/flags.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/flags.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -7149,7 +7149,7 @@ ENV2.registerFlag("IS_TEST", () => false);
 ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
 ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7238,7 +7238,7 @@ function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeri
   return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7285,7 +7285,7 @@ function op(f) {
   return f2;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -7311,7 +7311,7 @@ function complex_(real4, imag4) {
 }
 var complex = op({complex_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7357,7 +7357,7 @@ function makeTensor(values, shape, inferredShape, dtype) {
   return ENGINE.makeTensor(values, shape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7379,7 +7379,7 @@ function tensor(values, shape, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7406,7 +7406,7 @@ var DTYPE_VALUE_SIZE_MAP = {
   complex64: 8
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7708,7 +7708,7 @@ function getFloat16Decoder() {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7765,7 +7765,7 @@ var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(lou
 var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
 var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -7985,7 +7985,7 @@ var BrowserIndexedDBManager = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -8172,7 +8172,7 @@ var BrowserLocalStorageManager = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -8279,7 +8279,7 @@ async function moveModel(sourceURL, destURL) {
   return cloneModelInternal(sourceURL, destURL, deleteSource);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -8328,7 +8328,7 @@ if (env().get("IS_BROWSER")) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -8384,7 +8384,7 @@ if (env().get("IS_NODE")) {
   env().setPlatform("node", new PlatformNode());
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -8407,7 +8407,7 @@ function buffer(shape, dtype = "float32", values) {
   return new TensorBuffer(shape, dtype, values);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -8438,7 +8438,7 @@ function cast_(x, dtype) {
 }
 var cast = op({cast_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -8462,7 +8462,7 @@ function clone_(x) {
 }
 var clone = op({clone_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -8483,7 +8483,7 @@ function print2(x, verbose = false) {
   console.log(x.toString(verbose));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -8509,7 +8509,7 @@ var opHandler2 = {
 };
 setOpHandler(opHandler2);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js
 var io_exports = {};
 __export(io_exports, {
   browserFiles: () => browserFiles,
@@ -8534,7 +8534,7 @@ __export(io_exports, {
   withSaveHandler: () => withSaveHandler
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -8739,7 +8739,7 @@ function browserFiles(files) {
   return new BrowserFiles(files);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -8781,7 +8781,7 @@ function monitorPromisesProgress(promises, onProgress, startFraction, endFractio
   return Promise.all(promises.map(registerMonitor));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -8903,7 +8903,7 @@ Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9106,7 +9106,7 @@ function browserHTTPRequest(path, loadOptions) {
   return http(path, loadOptions);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9162,7 +9162,7 @@ function withSaveHandler(saveHandler) {
   return new PassthroughSaver(saveHandler);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9180,13 +9180,13 @@ function withSaveHandler(saveHandler) {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/math.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/math.js
 var math_exports = {};
 __export(math_exports, {
   confusionMatrix: () => confusionMatrix
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -9213,7 +9213,7 @@ function matMul_(a, b, transposeA = false, transposeB = false) {
 }
 var matMul = op({matMul_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -9241,7 +9241,7 @@ function oneHot_(indices, depth, onValue = 1, offValue = 0) {
 }
 var oneHot = op({oneHot_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9276,7 +9276,7 @@ function transpose_(x, perm) {
 }
 var transpose = op({transpose_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9309,7 +9309,7 @@ function confusionMatrix_(labels, predictions, numClasses) {
 }
 var confusionMatrix = op({confusionMatrix_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/math.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/math.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9327,7 +9327,7 @@ var confusionMatrix = op({confusionMatrix_});
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
 var browser_exports = {};
 __export(browser_exports, {
   fromPixels: () => fromPixels,
@@ -9335,7 +9335,7 @@ __export(browser_exports, {
   toPixels: () => toPixels
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -9367,7 +9367,7 @@ function tensor3d(values, shape, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -9551,7 +9551,7 @@ async function toPixels(img, canvas) {
 }
 var fromPixels = op({fromPixels_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
 var gather_nd_util_exports = {};
 __export(gather_nd_util_exports, {
   prepareAndValidate: () => prepareAndValidate
@@ -9595,7 +9595,7 @@ function prepareAndValidate(tensor2, indices) {
   return [resultShape, nResult, sliceSize, strides];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
 var scatter_nd_util_exports = {};
 __export(scatter_nd_util_exports, {
   calculateShapes: () => calculateShapes,
@@ -9664,7 +9664,7 @@ function calculateShapes(updates, indices, shape) {
   return {sliceRank, numUpdates, sliceSize, strides, outputSize};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
 var slice_util_exports = {};
 __export(slice_util_exports, {
   assertParamsValid: () => assertParamsValid,
@@ -9956,7 +9956,7 @@ function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask
   return {nonStrided, $begin, $end, $strides, size, newShape, outShape};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js
 var serialization_exports = {};
 __export(serialization_exports, {
   Serializable: () => Serializable,
@@ -10008,7 +10008,7 @@ function registerClass(cls) {
   SerializationMap.register(cls);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js
 var test_util_exports = {};
 __export(test_util_exports, {
   TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
@@ -10136,11 +10136,11 @@ function encodeStrings(a) {
   return a;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/version.js
 /** @license See the LICENSE file. */
 var version = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/globals.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/globals.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10226,7 +10226,7 @@ function setPlatform(platformName, platform) {
   env().setPlatform(platformName, platform);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10252,7 +10252,7 @@ function add_(a, b) {
 }
 var add2 = op({add_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10278,7 +10278,7 @@ function floorDiv_(a, b) {
 }
 var floorDiv = op({floorDiv_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10308,7 +10308,7 @@ function div_(a, b) {
 }
 var div = op({div_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10334,7 +10334,7 @@ function mul_(a, b) {
 }
 var mul = op({mul_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10363,7 +10363,7 @@ function abs_(x) {
 }
 var abs = op({abs_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10387,7 +10387,7 @@ function acos_(x) {
 }
 var acos = op({acos_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10411,7 +10411,7 @@ function acosh_(x) {
 }
 var acosh = op({acosh_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10448,7 +10448,7 @@ function addN_(tensors) {
 }
 var addN = op({addN_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10473,7 +10473,7 @@ function all_(x, axis = null, keepDims = false) {
 }
 var all = op({all_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10498,7 +10498,7 @@ function any_(x, axis = null, keepDims = false) {
 }
 var any = op({any_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -10523,7 +10523,7 @@ function argMax_(x, axis = 0) {
 }
 var argMax = op({argMax_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -10548,7 +10548,7 @@ function argMin_(x, axis = 0) {
 }
 var argMin = op({argMin_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10572,7 +10572,7 @@ function asin_(x) {
 }
 var asin = op({asin_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10596,7 +10596,7 @@ function asinh_(x) {
 }
 var asinh = op({asinh_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10620,7 +10620,7 @@ function atan_(x) {
 }
 var atan = op({atan_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10646,7 +10646,7 @@ function atan2_(a, b) {
 }
 var atan2 = op({atan2_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -10670,7 +10670,7 @@ function atanh_(x) {
 }
 var atanh = op({atanh_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -10983,7 +10983,7 @@ function convertConv2DDataFormat(dataFormat) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11008,7 +11008,7 @@ function reshape_(x, shape) {
 }
 var reshape = op({reshape_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11050,7 +11050,7 @@ function avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {
 }
 var avgPool = op({avgPool_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11091,7 +11091,7 @@ function avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat =
 }
 var avgPool3d = op({avgPool3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11128,7 +11128,7 @@ function concat_(tensors, axis = 0) {
 }
 var concat = op({concat_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11152,7 +11152,7 @@ function sigmoid_(x) {
 }
 var sigmoid = op({sigmoid_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11180,7 +11180,7 @@ function slice_(x, begin, size) {
 }
 var slice = op({slice_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11204,7 +11204,7 @@ function tanh_(x) {
 }
 var tanh2 = op({tanh_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11244,7 +11244,7 @@ function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
 }
 var basicLSTMCell = op({basicLSTMCell_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11273,7 +11273,7 @@ function batchToSpaceND_(x, blockShape, crops) {
 }
 var batchToSpaceND = op({batchToSpaceND_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
 function xAs4D(x) {
   let x4D;
   if (x.rank === 0 || x.rank === 1) {
@@ -11288,7 +11288,7 @@ function xAs4D(x) {
   return x4D;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11337,7 +11337,7 @@ function batchNorm_(x, mean4, variance, offset, scale2, varianceEpsilon) {
 }
 var batchNorm = op({batchNorm_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
 function batchNorm2d_(x, mean4, variance, offset, scale2, varianceEpsilon) {
   const $x = convertToTensor(x, "x", "batchNorm");
   const $mean = convertToTensor(mean4, "mean", "batchNorm");
@@ -11363,7 +11363,7 @@ function batchNorm2d_(x, mean4, variance, offset, scale2, varianceEpsilon) {
 }
 var batchNorm2d = op({batchNorm2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
 function batchNorm3d_(x, mean4, variance, offset, scale2, varianceEpsilon) {
   const $x = convertToTensor(x, "x", "batchNorm");
   const $mean = convertToTensor(mean4, "mean", "batchNorm");
@@ -11389,7 +11389,7 @@ function batchNorm3d_(x, mean4, variance, offset, scale2, varianceEpsilon) {
 }
 var batchNorm3d = op({batchNorm3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
 function batchNorm4d_(x, mean4, variance, offset, scale2, varianceEpsilon) {
   const $x = convertToTensor(x, "x", "batchNorm");
   const $mean = convertToTensor(mean4, "mean", "batchNorm");
@@ -11415,7 +11415,7 @@ function batchNorm4d_(x, mean4, variance, offset, scale2, varianceEpsilon) {
 }
 var batchNorm4d = op({batchNorm4d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11444,7 +11444,7 @@ function bincount_(x, weights, size) {
 }
 var bincount = op({bincount_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11496,7 +11496,7 @@ function broadcastTo_(x, shape) {
 }
 var broadcastTo = op({broadcastTo_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11520,7 +11520,7 @@ function ceil_(x) {
 }
 var ceil = op({ceil_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11546,31 +11546,31 @@ function clipByValue_(x, clipValueMin, clipValueMax) {
 }
 var clipByValue = op({clipByValue_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
 function concat1d_(tensors) {
   return concat(tensors, 0);
 }
 var concat1d = op({concat1d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
 function concat2d_(tensors, axis) {
   return concat(tensors, axis);
 }
 var concat2d = op({concat2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
 function concat3d_(tensors, axis) {
   return concat(tensors, axis);
 }
 var concat3d = op({concat3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
 function concat4d_(tensors, axis) {
   return concat(tensors, axis);
 }
 var concat4d = op({concat4d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11614,7 +11614,7 @@ function conv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1,
 }
 var conv2d = op({conv2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
 function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
   const $x = convertToTensor(x, "x", "conv1d");
   const $filter = convertToTensor(filter, "filter", "conv1d");
@@ -11645,7 +11645,7 @@ function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimR
 }
 var conv1d = op({conv1d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11692,7 +11692,7 @@ function conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = "N
 }
 var conv2DBackpropInput = op({conv2DBackpropInput_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
 function conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {
   const $x = convertToTensor(x, "x", "conv2dTranspose");
   const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
@@ -11700,7 +11700,7 @@ function conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode
 }
 var conv2dTranspose = op({conv2dTranspose_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11741,7 +11741,7 @@ function conv3d_(x, filter, strides, pad3, dataFormat = "NDHWC", dilations = [1,
 }
 var conv3d = op({conv3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11785,7 +11785,7 @@ function conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {
 }
 var conv3DBackpropInput = op({conv3DBackpropInput_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
 function conv3dTranspose_(x, filter, outputShape, strides, pad3) {
   const $x = convertToTensor(x, "x", "conv3dTranspose");
   const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
@@ -11793,7 +11793,7 @@ function conv3dTranspose_(x, filter, outputShape, strides, pad3) {
 }
 var conv3dTranspose = op({conv3dTranspose_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11817,7 +11817,7 @@ function cos_(x) {
 }
 var cos = op({cos_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11841,7 +11841,7 @@ function cosh_(x) {
 }
 var cosh = op({cosh_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -11866,7 +11866,7 @@ function cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {
 }
 var cumsum = op({cumsum_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11896,7 +11896,7 @@ function denseBincount_(x, weights, size, binaryOutput = false) {
 }
 var denseBincount = op({denseBincount_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11931,7 +11931,7 @@ function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
 }
 var depthToSpace = op({depthToSpace_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11973,7 +11973,7 @@ function depthwiseConv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilatio
 }
 var depthwiseConv2d = op({depthwiseConv2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -11997,7 +11997,7 @@ function diag_(x) {
 }
 var diag = op({diag_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12036,7 +12036,7 @@ function dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat =
 }
 var dilation2d = op({dilation2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -12104,7 +12104,7 @@ function assertAndGetBroadcastShape(shapeA, shapeB) {
   return result;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12131,7 +12131,7 @@ function equal_(a, b) {
 }
 var equal = op({equal_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12170,7 +12170,7 @@ function where_(condition, a, b) {
 }
 var where = op({where_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12194,7 +12194,7 @@ function zerosLike_(x) {
 }
 var zerosLike = op({zerosLike_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12222,7 +12222,7 @@ function divNoNan_(a, b) {
 }
 var divNoNan = op({divNoNan_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12268,7 +12268,7 @@ function dot_(t1, t2) {
 }
 var dot = op({dot_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12292,7 +12292,7 @@ function elu_(x) {
 }
 var elu = op({elu_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12320,7 +12320,7 @@ function erf_(x) {
 }
 var erf = op({erf_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12344,7 +12344,7 @@ function exp_(x) {
 }
 var exp = op({exp_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12370,7 +12370,7 @@ function expandDims_(x, axis = 0) {
 }
 var expandDims = op({expandDims_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12394,7 +12394,7 @@ function expm1_(x) {
 }
 var expm1 = op({expm1_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12420,7 +12420,7 @@ function tile_(x, reps) {
 }
 var tile = op({tile_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12469,7 +12469,7 @@ function eye_(numRows, numColumns, batchShape, dtype = "float32") {
 }
 var eye = op({eye_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12491,7 +12491,7 @@ function fill(shape, value, dtype) {
   return ENGINE.runKernel(Fill, {}, attrs);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12515,7 +12515,7 @@ function floor_(x) {
 }
 var floor = op({floor_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12541,7 +12541,7 @@ function gather_(x, indices, axis = 0, batchDims = 0) {
 }
 var gather = op({gather_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12568,7 +12568,7 @@ function greater_(a, b) {
 }
 var greater = op({greater_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12595,7 +12595,7 @@ function greaterEqual_(a, b) {
 }
 var greaterEqual = op({greaterEqual_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12619,7 +12619,7 @@ function imag_(input2) {
 }
 var imag = op({imag_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12643,7 +12643,7 @@ function isFinite_(x) {
 }
 var isFinite2 = op({isFinite_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12667,7 +12667,7 @@ function isInf_(x) {
 }
 var isInf = op({isInf_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12691,7 +12691,7 @@ function isNaN_(x) {
 }
 var isNaN2 = op({isNaN_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12716,7 +12716,7 @@ function leakyRelu_(x, alpha = 0.2) {
 }
 var leakyRelu = op({leakyRelu_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12743,7 +12743,7 @@ function less_(a, b) {
 }
 var less = op({less_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12770,7 +12770,7 @@ function lessEqual_(a, b) {
 }
 var lessEqual = op({lessEqual_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12795,7 +12795,7 @@ function linspace(start, stop, num) {
   return ENGINE.runKernel(LinSpace, {}, attrs);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -12834,7 +12834,7 @@ function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, be
 }
 var localResponseNormalization = op({localResponseNormalization_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12858,7 +12858,7 @@ function log_(x) {
 }
 var log = op({log_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12882,7 +12882,7 @@ function log1p_(x) {
 }
 var log1p = op({log1p_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -12993,7 +12993,7 @@ function checkGrads(grads2) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13017,7 +13017,7 @@ function neg_(x) {
 }
 var neg = op({neg_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13041,7 +13041,7 @@ function softplus_(x) {
 }
 var softplus = op({softplus_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13072,7 +13072,7 @@ function logSigmoid_(x) {
 }
 var logSigmoid = op({logSigmoid_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13097,7 +13097,7 @@ function max_(x, axis = null, keepDims = false) {
 }
 var max = op({max_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13123,7 +13123,7 @@ function sub_(a, b) {
 }
 var sub = op({sub_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13151,7 +13151,7 @@ function sum_(x, axis = null, keepDims = false) {
 }
 var sum2 = op({sum_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -13194,7 +13194,7 @@ function logSoftmax_(logits, axis = -1) {
 }
 var logSoftmax = op({logSoftmax_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -13275,7 +13275,7 @@ function getInnerMostAxes(numAxes, rank) {
   return res;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13309,7 +13309,7 @@ function logSumExp_(x, axis = null, keepDims = false) {
 }
 var logSumExp = op({logSumExp_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13335,7 +13335,7 @@ function logicalAnd_(a, b) {
 }
 var logicalAnd = op({logicalAnd_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13359,7 +13359,7 @@ function logicalNot_(x) {
 }
 var logicalNot = op({logicalNot_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13385,7 +13385,7 @@ function logicalOr_(a, b) {
 }
 var logicalOr = op({logicalOr_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13410,7 +13410,7 @@ function logicalXor_(a, b) {
 }
 var logicalXor = op({logicalXor_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13451,7 +13451,7 @@ function maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {
 }
 var maxPool = op({maxPool_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13491,7 +13491,7 @@ function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, d
 }
 var maxPool3d = op({maxPool3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13517,7 +13517,7 @@ function maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex =
 }
 var maxPoolWithArgmax = op({maxPoolWithArgmax_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13548,7 +13548,7 @@ function maximum_(a, b) {
 }
 var maximum = op({maximum_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -13573,7 +13573,7 @@ function mean_(x, axis = null, keepDims = false) {
 }
 var mean = op({mean_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -13598,7 +13598,7 @@ function min_(x, axis = null, keepDims = false) {
 }
 var min = op({min_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13629,7 +13629,7 @@ function minimum_(a, b) {
 }
 var minimum = op({minimum_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13664,7 +13664,7 @@ function mirrorPad_(x, paddings, mode) {
 }
 var mirrorPad = op({mirrorPad_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13690,7 +13690,7 @@ function mod_(a, b) {
 }
 var mod = op({mod_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -13714,7 +13714,7 @@ function square_(x) {
 }
 var square = op({square_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13745,7 +13745,7 @@ function moments_(x, axis = null, keepDims = false) {
 }
 var moments = op({moments_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
 function multiRNNCell_(lstmCells, data, c, h) {
   const $data = convertToTensor(data, "data", "multiRNNCell");
   const $c = convertToTensorArray(c, "c", "multiRNNCell");
@@ -13768,7 +13768,7 @@ function multiRNNCell_(lstmCells, data, c, h) {
 }
 var multiRNNCell = op({multiRNNCell_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13804,7 +13804,7 @@ function multinomial_(logits, numSamples, seed, normalized = false) {
 }
 var multinomial = op({multinomial_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13831,7 +13831,7 @@ function notEqual_(a, b) {
 }
 var notEqual = op({notEqual_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13858,7 +13858,7 @@ function zeros(shape, dtype = "float32") {
   return ENGINE.makeTensor(values, shape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13885,7 +13885,7 @@ function ones2(shape, dtype = "float32") {
   return ENGINE.makeTensor(values, shape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -13909,7 +13909,7 @@ function onesLike_(x) {
 }
 var onesLike = op({onesLike_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
 function outerProduct_(v1, v2) {
   const $v1 = convertToTensor(v1, "v1", "outerProduct");
   const $v2 = convertToTensor(v2, "v2", "outerProduct");
@@ -13920,7 +13920,7 @@ function outerProduct_(v1, v2) {
 }
 var outerProduct = op({outerProduct_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -13948,35 +13948,35 @@ function pad_(x, paddings, constantValue = 0) {
 }
 var pad = op({pad_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
 function pad1d_(x, paddings, constantValue = 0) {
   assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
   return pad(x, [paddings], constantValue);
 }
 var pad1d = op({pad1d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
 function pad2d_(x, paddings, constantValue = 0) {
   assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
   return pad(x, paddings, constantValue);
 }
 var pad2d = op({pad2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
 function pad3d_(x, paddings, constantValue = 0) {
   assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
   return pad(x, paddings, constantValue);
 }
 var pad3d = op({pad3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
 function pad4d_(x, paddings, constantValue = 0) {
   assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
   return pad(x, paddings, constantValue);
 }
 var pad4d = op({pad4d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14009,7 +14009,7 @@ function spaceToBatchND_(x, blockShape, paddings) {
 }
 var spaceToBatchND = op({spaceToBatchND_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14087,7 +14087,7 @@ function withSpaceToBatchBasePaddings(filterShape, dilation) {
 }
 var pool = op({pool_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14113,7 +14113,7 @@ function pow_(base2, exp4) {
 }
 var pow = op({pow_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14138,7 +14138,7 @@ function prelu_(x, alpha) {
 }
 var prelu = op({prelu_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14166,7 +14166,7 @@ function prod_(x, axis = null, keepDims = false) {
 }
 var prod = op({prod_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14202,7 +14202,7 @@ function rand_(shape, randFunction, dtype) {
 }
 var rand = op({rand_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
 var seedrandom = __toModule(require_seedrandom2());
 /**
  * @license
@@ -14343,7 +14343,7 @@ var UniformRandom = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14379,7 +14379,7 @@ function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
 }
 var randomGamma = op({randomGamma_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14409,7 +14409,7 @@ function randomNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) {
 }
 var randomNormal = op({randomNormal_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14436,7 +14436,7 @@ function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed)
 }
 var randomUniform = op({randomUniform_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14461,7 +14461,7 @@ function range(start, stop, step5 = 1, dtype = "float32") {
   return ENGINE.runKernel(Range, {}, attrs);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14485,7 +14485,7 @@ function real_(input2) {
 }
 var real = op({real_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14509,7 +14509,7 @@ function reciprocal_(x) {
 }
 var reciprocal = op({reciprocal_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14533,7 +14533,7 @@ function relu_(x) {
 }
 var relu = op({relu_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14557,7 +14557,7 @@ function relu6_(x) {
 }
 var relu6 = op({relu6_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14582,7 +14582,7 @@ function reverse_(x, axis) {
 }
 var reverse = op({reverse_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14606,7 +14606,7 @@ function reverse1d_(x) {
 }
 var reverse1d = op({reverse1d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14630,7 +14630,7 @@ function reverse2d_(x, axis) {
 }
 var reverse2d = op({reverse2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14654,7 +14654,7 @@ function reverse3d_(x, axis) {
 }
 var reverse3d = op({reverse3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14678,7 +14678,7 @@ function reverse4d_(x, axis) {
 }
 var reverse4d = op({reverse4d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14702,7 +14702,7 @@ function round_(x) {
 }
 var round2 = op({round_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14726,7 +14726,7 @@ function rsqrt_(x) {
 }
 var rsqrt = op({rsqrt_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14755,7 +14755,7 @@ function scalar(value, dtype) {
   return makeTensor(value, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -14779,7 +14779,7 @@ function selu_(x) {
 }
 var selu = op({selu_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
 function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = "NHWC") {
   const $x = convertToTensor(x, "x", "separableConv2d");
   const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
@@ -14811,7 +14811,7 @@ function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, di
 }
 var separableConv2d = op({separableConv2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -14856,7 +14856,7 @@ async function setdiff1dAsync_(x, y) {
 }
 var setdiff1dAsync = setdiff1dAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14880,7 +14880,7 @@ function sign_(x) {
 }
 var sign = op({sign_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14904,7 +14904,7 @@ function sin_(x) {
 }
 var sin = op({sin_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14928,7 +14928,7 @@ function sinh_(x) {
 }
 var sinh = op({sinh_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14952,7 +14952,7 @@ function slice1d_(x, begin, size) {
 }
 var slice1d = op({slice1d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -14976,7 +14976,7 @@ function slice2d_(x, begin, size) {
 }
 var slice2d = op({slice2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15000,7 +15000,7 @@ function slice3d_(x, begin, size) {
 }
 var slice3d = op({slice3d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15024,7 +15024,7 @@ function slice4d_(x, begin, size) {
 }
 var slice4d = op({slice4d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15055,7 +15055,7 @@ function softmax_(logits, dim = -1) {
 }
 var softmax = op({softmax_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15079,7 +15079,7 @@ function fft_(input2) {
 }
 var fft = op({fft_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15103,7 +15103,7 @@ function ifft_(input2) {
 }
 var ifft = op({ifft_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15149,7 +15149,7 @@ function irfft_(input2) {
 }
 var irfft = op({irfft_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15174,7 +15174,7 @@ function split_(x, numOrSizeSplits, axis = 0) {
 }
 var split = op({split_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15224,7 +15224,7 @@ function rfft_(input2, fftLength) {
 }
 var rfft = op({rfft_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15248,7 +15248,7 @@ function sqrt_(x) {
 }
 var sqrt = op({sqrt_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15276,7 +15276,7 @@ function squaredDifference_(a, b) {
 }
 var squaredDifference = op({squaredDifference_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15299,7 +15299,7 @@ function squeeze_(x, axis) {
 }
 var squeeze = op({squeeze_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15328,7 +15328,7 @@ function stack_(tensors, axis = 0) {
 }
 var stack = op({stack_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15353,7 +15353,7 @@ function step_(x, alpha = 0) {
 }
 var step = op({step_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15387,7 +15387,7 @@ function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellip
 }
 var stridedSlice = op({stridedSlice_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15411,7 +15411,7 @@ function tan_(x) {
 }
 var tan = op({tan_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15438,7 +15438,7 @@ function tensor1d(values, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15470,7 +15470,7 @@ function tensor2d(values, shape, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15502,7 +15502,7 @@ function tensor4d(values, shape, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15534,7 +15534,7 @@ function tensor5d(values, shape, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15567,7 +15567,7 @@ function tensor6d(values, shape, dtype) {
   return makeTensor(values, shape, inferredShape, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15600,7 +15600,7 @@ function topk_(x, k = 1, sorted = true) {
 }
 var topk = op({topk_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15630,7 +15630,7 @@ function truncatedNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) {
 }
 var truncatedNormal = op({truncatedNormal_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15657,7 +15657,7 @@ function unique_(x, axis = 0) {
 }
 var unique = op({unique_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15684,7 +15684,7 @@ function unsortedSegmentSum_(x, segmentIds, numSegments) {
 }
 var unsortedSegmentSum = op({unsortedSegmentSum_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15710,7 +15710,7 @@ function unstack_(x, axis = 0) {
 }
 var unstack = op({unstack_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15731,7 +15731,7 @@ function variable(initialValue, trainable = true, name, dtype) {
   return ENGINE.makeVariable(initialValue, trainable, name, dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15765,7 +15765,7 @@ function whereImpl(condShape, condVals) {
   return out.toTensor();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -15793,7 +15793,7 @@ async function whereAsync_(condition) {
 }
 var whereAsync = whereAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15842,7 +15842,7 @@ async function booleanMaskAsync_(tensor2, mask, axis) {
 }
 var booleanMaskAsync = booleanMaskAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15910,7 +15910,7 @@ function normImpl(x, p2, axis = null) {
 }
 var norm = op({norm_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15945,7 +15945,7 @@ function movingAverage_(v, x, decay, step5, zeroDebias = true) {
 }
 var movingAverage = op({movingAverage_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -15972,7 +15972,7 @@ function scatterND_(indices, updates, shape) {
 }
 var scatterND = op({scatterND_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
 function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
   if (sparseIndices.dtype !== "int32") {
     throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
@@ -15994,7 +15994,7 @@ function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues)
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -16026,7 +16026,7 @@ function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue =
 }
 var sparseToDense = op({sparseToDense_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -16051,7 +16051,7 @@ function gatherND_(x, indices) {
 }
 var gatherND = op({gatherND_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16089,7 +16089,7 @@ function getNoiseShape(x, noiseShape) {
   return noiseShape;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -16120,7 +16120,7 @@ function dropout_(x, rate, noiseShape, seed) {
 }
 var dropout = op({dropout_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16150,7 +16150,7 @@ function cosineWindow(windowLength, a, b) {
   return tensor1d(newValues, "float32");
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16205,7 +16205,7 @@ async function inTopKAsync_(predictions, targets, k = 1) {
 }
 var inTopKAsync = inTopKAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
 var fused_ops_exports = {};
 __export(fused_ops_exports, {
   conv2d: () => conv2d2,
@@ -16213,7 +16213,7 @@ __export(fused_ops_exports, {
   matMul: () => matMul2
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16255,7 +16255,7 @@ function conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = "
 }
 var conv2DBackpropFilter = op({conv2DBackpropFilter_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16310,7 +16310,7 @@ var shouldFuse = (gradientDepth, activation2) => {
   return !gradientMode || activation2 === "linear";
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16415,7 +16415,7 @@ function fusedConv2d_({x, filter, strides, pad: pad3, dataFormat = "NHWC", dilat
 }
 var conv2d2 = op({fusedConv2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16447,7 +16447,7 @@ function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3,
 }
 var depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16481,7 +16481,7 @@ function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3,
 }
 var depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16586,7 +16586,7 @@ function fusedDepthwiseConv2d_({x, filter, strides, pad: pad3, dataFormat = "NHW
 }
 var depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16688,7 +16688,7 @@ function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activ
 }
 var matMul2 = op({fusedMatMul_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16706,7 +16706,7 @@ var matMul2 = op({fusedMatMul_});
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16728,7 +16728,7 @@ function hammingWindow_(windowLength) {
 }
 var hammingWindow = op({hammingWindow_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16750,7 +16750,7 @@ function hannWindow_(windowLength) {
 }
 var hannWindow = op({hannWindow_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16792,7 +16792,7 @@ function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
 }
 var frame = op({frame_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -16823,7 +16823,7 @@ function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow
 }
 var stft = op({stft_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16858,7 +16858,7 @@ function cropAndResize_(image3, boxes, boxInd, cropSize, method = "bilinear", ex
 }
 var cropAndResize = op({cropAndResize_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16884,7 +16884,7 @@ function flipLeftRight_(image3) {
 }
 var flipLeftRight = op({flipLeftRight_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16911,7 +16911,7 @@ function rotateWithOffset_(image3, radians, fillValue = 0, center = 0.5) {
 }
 var rotateWithOffset = op({rotateWithOffset_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16949,7 +16949,7 @@ function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, score
   return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -16978,7 +16978,7 @@ function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, sc
 }
 var nonMaxSuppression = op({nonMaxSuppression_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -17024,7 +17024,7 @@ function binarySearch_(arr, target, comparator) {
   return found ? left : -left - 1;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17135,7 +17135,7 @@ function ascendingComparator(c1, c2) {
   return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17173,7 +17173,7 @@ async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshol
 }
 var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17205,7 +17205,7 @@ function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold
 }
 var nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17247,7 +17247,7 @@ async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, io
 }
 var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17283,7 +17283,7 @@ function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0
 }
 var nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17322,7 +17322,7 @@ async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouTh
 }
 var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17361,7 +17361,7 @@ function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters =
 }
 var resizeBilinear = op({resizeBilinear_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17401,7 +17401,7 @@ function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCen
 }
 var resizeNearestNeighbor = op({resizeNearestNeighbor_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -17430,7 +17430,7 @@ function transform_(image3, transforms, interpolation = "nearest", fillMode = "c
 }
 var transform = op({transform_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17475,7 +17475,7 @@ function bandPart_(a, numLower, numUpper) {
 }
 var bandPart = op({bandPart_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17528,7 +17528,7 @@ function gramSchmidt_(xs) {
 }
 var gramSchmidt = op({gramSchmidt_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17628,7 +17628,7 @@ function qr2d(x, fullMatrices = false) {
 }
 var qr = op({qr_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17653,7 +17653,7 @@ var Reduction;
   Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
 })(Reduction || (Reduction = {}));
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
 function computeWeightedLoss_(losses4, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
   const $losses = convertToTensor(losses4, "losses", "computeWeightedLoss");
   let $weights = null;
@@ -17689,7 +17689,7 @@ function computeWeightedLoss_(losses4, weights, reduction2 = Reduction.SUM_BY_NO
 }
 var computeWeightedLoss = op({computeWeightedLoss_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17719,7 +17719,7 @@ function absoluteDifference_(labels, predictions, weights, reduction2 = Reductio
 }
 var absoluteDifference = op({absoluteDifference_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
 function cosineDistance_(labels, predictions, axis, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
   const $labels = convertToTensor(labels, "labels", "cosineDistance");
   const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
@@ -17734,7 +17734,7 @@ function cosineDistance_(labels, predictions, axis, weights, reduction2 = Reduct
 }
 var cosineDistance = op({cosineDistance_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
 function hingeLoss_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
   let $labels = convertToTensor(labels, "labels", "hingeLoss");
   const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
@@ -17750,7 +17750,7 @@ function hingeLoss_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_
 }
 var hingeLoss = op({hingeLoss_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17784,7 +17784,7 @@ function huberLoss_(labels, predictions, weights, delta = 1, reduction2 = Reduct
 }
 var huberLoss = op({huberLoss_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17818,7 +17818,7 @@ function logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction2 = Re
 }
 var logLoss = op({logLoss_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17848,7 +17848,7 @@ function meanSquaredError_(labels, predictions, weights, reduction2 = Reduction.
 }
 var meanSquaredError = op({meanSquaredError_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17893,7 +17893,7 @@ function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing
 }
 var sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -17955,7 +17955,7 @@ function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0,
 }
 var softmaxCrossEntropy = op({softmaxCrossEntropy_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -18015,7 +18015,7 @@ var losses = {
   softmaxCrossEntropy
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18092,7 +18092,7 @@ Object.defineProperty(Optimizer, Symbol.hasInstance, {
   }
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18193,7 +18193,7 @@ var AdadeltaOptimizer = class extends Optimizer {
 AdadeltaOptimizer.className = "Adadelta";
 registerClass(AdadeltaOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18268,7 +18268,7 @@ var AdagradOptimizer = class extends Optimizer {
 AdagradOptimizer.className = "Adagrad";
 registerClass(AdagradOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18388,7 +18388,7 @@ var AdamOptimizer = class extends Optimizer {
 AdamOptimizer.className = "Adam";
 registerClass(AdamOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18495,7 +18495,7 @@ var AdamaxOptimizer = class extends Optimizer {
 AdamaxOptimizer.className = "Adamax";
 registerClass(AdamaxOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18562,7 +18562,7 @@ var SGDOptimizer = class extends Optimizer {
 SGDOptimizer.className = "SGD";
 registerClass(SGDOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18649,7 +18649,7 @@ var MomentumOptimizer = class extends SGDOptimizer {
 MomentumOptimizer.className = "Momentum";
 registerClass(MomentumOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18790,7 +18790,7 @@ var RMSPropOptimizer = class extends Optimizer {
 RMSPropOptimizer.className = "RMSProp";
 registerClass(RMSPropOptimizer);
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18831,7 +18831,7 @@ var OptimizerConstructors = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/train.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/train.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -18858,7 +18858,7 @@ var train = {
   adam: OptimizerConstructors.adam
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -18887,7 +18887,7 @@ function nextFrame() {
   return new Promise((resolve) => delayCallback(() => resolve()));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
 var backend_util_exports = {};
 __export(backend_util_exports, {
   ERF_A1: () => ERF_A1,
@@ -18954,7 +18954,7 @@ __export(backend_util_exports, {
   warn: () => warn
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -18992,7 +18992,7 @@ function computeOutShape2(shapes, axis) {
   return outputShape;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -19017,7 +19017,7 @@ function computeOptimalWindowSize(inSize) {
   return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19040,7 +19040,7 @@ function getImageCenter(center, imageHeight, imageWidth) {
   return [centerX, centerY];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -19136,7 +19136,7 @@ function getSliceSize(uncroppedShape, crops, blockShape) {
   return sliceSize;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -19156,7 +19156,7 @@ function getSliceSize(uncroppedShape, crops, blockShape) {
 var SELU_SCALEALPHA = 1.7580993408473768;
 var SELU_SCALE = 1.0507009873554805;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -19180,7 +19180,7 @@ var ERF_A3 = 1.421413741;
 var ERF_A4 = -1.453152027;
 var ERF_A5 = 1.061405429;
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/log.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/log.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -19208,7 +19208,7 @@ function log2(...msg) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -19291,7 +19291,7 @@ function exponent(k, n, inverse) {
   return {real: real4, imag: imag4};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
 function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
   let splitSizes = [];
   if (typeof numOrSizeSplits === "number") {
@@ -19316,7 +19316,7 @@ function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
   return splitSizes;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
 var segment_util_exports = {};
 __export(segment_util_exports, {
   collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
@@ -19415,7 +19415,7 @@ function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
   return {batchSize, sliceSize, outerSize, dimSize, outputShape};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -19443,7 +19443,7 @@ function fromStringArrayToUint8(strings) {
   return strings.map((s) => encodeString(s));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
 var kernel_impls_exports = {};
 __export(kernel_impls_exports, {
   nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
@@ -19468,7 +19468,7 @@ __export(kernel_impls_exports, {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/base.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/base.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -19486,7 +19486,7 @@ __export(kernel_impls_exports, {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/index.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -19504,7 +19504,7 @@ __export(kernel_impls_exports, {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19530,7 +19530,7 @@ var absGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19562,7 +19562,7 @@ var acosGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19593,7 +19593,7 @@ var acoshGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19636,7 +19636,7 @@ var addGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19665,7 +19665,7 @@ var addNGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -19691,7 +19691,7 @@ var argMaxGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -19717,7 +19717,7 @@ var argMinGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19743,7 +19743,7 @@ var asinGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19774,7 +19774,7 @@ var asinhGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19819,7 +19819,7 @@ var atan2GradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19845,7 +19845,7 @@ var atanGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19871,7 +19871,7 @@ var atanhGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19920,7 +19920,7 @@ function avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode)
 }
 var avgPool3dGrad = op({avgPool3dGrad_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19949,7 +19949,7 @@ var avgPool3DGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -19990,7 +19990,7 @@ function avgPoolGrad_(dy, input2, filterSize, strides, pad3) {
 }
 var avgPoolGrad = op({avgPoolGrad_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20017,7 +20017,7 @@ var avgPoolGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20064,7 +20064,7 @@ var batchMatMulGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20089,7 +20089,7 @@ var batchToSpaceNDGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20130,7 +20130,7 @@ var broadcastToGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20154,7 +20154,7 @@ var castGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20178,7 +20178,7 @@ var ceilGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20207,7 +20207,7 @@ var clipByValueGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20230,7 +20230,7 @@ var complexAbsGradConfig = {
   gradFunc: absGradConfig.gradFunc
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20260,7 +20260,7 @@ var concatGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20291,7 +20291,7 @@ var conv2DGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20321,7 +20321,7 @@ var conv2DBackpropInputGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20358,7 +20358,7 @@ function conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {
 }
 var conv3DBackpropFilter = op({conv3DBackpropFilter_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20389,7 +20389,7 @@ var conv3DGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20415,7 +20415,7 @@ var cosGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20441,7 +20441,7 @@ var coshGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20477,7 +20477,7 @@ var cumsumGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20516,7 +20516,7 @@ var depthwiseConv2dNativeGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20547,7 +20547,7 @@ var dilation2dGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20574,7 +20574,7 @@ var eluGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20601,7 +20601,7 @@ var erfGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20627,7 +20627,7 @@ var expGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20653,7 +20653,7 @@ var expandDimsGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20679,7 +20679,7 @@ var expm1GradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20703,7 +20703,7 @@ var floorGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20747,7 +20747,7 @@ var floorDivGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20829,7 +20829,7 @@ var fusedBatchNormGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20892,7 +20892,7 @@ function arrayConcat(arrays) {
   return result;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20918,7 +20918,7 @@ var greaterEqualGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20942,7 +20942,7 @@ var identityGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20966,7 +20966,7 @@ var isFiniteGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -20990,7 +20990,7 @@ var isInfGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21014,7 +21014,7 @@ var isNanGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21042,7 +21042,7 @@ var leakyReluGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21068,7 +21068,7 @@ var log1pGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21094,7 +21094,7 @@ var logGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21128,7 +21128,7 @@ var logSoftmaxGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21152,7 +21152,7 @@ function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1
 }
 var localResponseNormalizationBackprop = op({localResponseNormalizationBackprop_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21182,7 +21182,7 @@ var lrnGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21214,7 +21214,7 @@ function gradForMinAndMax(dy, y, xOrig, origAxes) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21250,7 +21250,7 @@ var maxGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21278,7 +21278,7 @@ var maximumGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21337,7 +21337,7 @@ function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundi
 }
 var maxPool3dGrad = op({maxPool3dGrad_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21367,7 +21367,7 @@ var maxPool3DGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21400,7 +21400,7 @@ function maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRounding
 }
 var maxPoolGrad = op({maxPoolGrad_});
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21430,7 +21430,7 @@ var maxPoolGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21470,7 +21470,7 @@ var meanGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21505,7 +21505,7 @@ var minGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21533,7 +21533,7 @@ var minimumGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21561,7 +21561,7 @@ var mirrorPadGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21603,7 +21603,7 @@ var modGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21646,7 +21646,7 @@ var multiplyGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21670,7 +21670,7 @@ var negGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21696,7 +21696,7 @@ var oneHotGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21720,7 +21720,7 @@ var onesLikeGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21747,7 +21747,7 @@ var packGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21775,7 +21775,7 @@ var padV2GradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21824,7 +21824,7 @@ var powGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21861,7 +21861,7 @@ var preluGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21905,7 +21905,7 @@ var divGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21931,7 +21931,7 @@ var reciprocalGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21958,7 +21958,7 @@ var relu6GradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -21984,7 +21984,7 @@ var reluGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -22010,7 +22010,7 @@ var reshapeGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22038,7 +22038,7 @@ var resizeBilinearGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22066,7 +22066,7 @@ var resizeNearestNeighborGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22092,7 +22092,7 @@ var reverseGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22116,7 +22116,7 @@ var roundGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22142,7 +22142,7 @@ var rsqrtGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22172,7 +22172,7 @@ var selectGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22207,7 +22207,7 @@ var seluGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22233,7 +22233,7 @@ var sigmoidGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22257,7 +22257,7 @@ var signGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22283,7 +22283,7 @@ var sinGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22309,7 +22309,7 @@ var sinhGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22342,7 +22342,7 @@ var sliceGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22373,7 +22373,7 @@ var softmaxGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22399,7 +22399,7 @@ var softplusGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22424,7 +22424,7 @@ var spaceToBatchNDGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22449,7 +22449,7 @@ var splitVGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22475,7 +22475,7 @@ var sqrtGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -22501,7 +22501,7 @@ var squareGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22530,7 +22530,7 @@ var squaredDifferenceGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22554,7 +22554,7 @@ var stepGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22597,7 +22597,7 @@ var subGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -22631,7 +22631,7 @@ var sumGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22657,7 +22657,7 @@ var tanGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22683,7 +22683,7 @@ var tanhGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22753,7 +22753,7 @@ var tileGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22780,7 +22780,7 @@ var transposeGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -22806,7 +22806,7 @@ var unpackGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22847,7 +22847,7 @@ function gatherDropNegatives(x, indices) {
   return where(isPositive, gathered, zeroSlice);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22871,7 +22871,7 @@ var zerosLikeGradConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -22998,7 +22998,7 @@ for (const gradientConfig of gradConfigs) {
   registerGradient(gradientConfig);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23020,7 +23020,7 @@ getGlobalTensorClass().prototype.abs = function() {
   return abs(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23042,7 +23042,7 @@ getGlobalTensorClass().prototype.acos = function() {
   return acos(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23064,7 +23064,7 @@ getGlobalTensorClass().prototype.acosh = function() {
   return acosh(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23086,7 +23086,7 @@ getGlobalTensorClass().prototype.add = function(b) {
   return add2(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23108,7 +23108,7 @@ getGlobalTensorClass().prototype.all = function(axis, keepDims) {
   return all(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23130,7 +23130,7 @@ getGlobalTensorClass().prototype.any = function(axis, keepDims) {
   return any(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23152,7 +23152,7 @@ getGlobalTensorClass().prototype.argMax = function(axis) {
   return argMax(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23174,7 +23174,7 @@ getGlobalTensorClass().prototype.argMin = function(axis) {
   return argMin(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23197,7 +23197,7 @@ getGlobalTensorClass().prototype.asScalar = function() {
   return reshape(this, []);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23219,7 +23219,7 @@ getGlobalTensorClass().prototype.asType = function(dtype) {
   return cast(this, dtype);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23241,7 +23241,7 @@ getGlobalTensorClass().prototype.as1D = function() {
   return reshape(this, [this.size]);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23263,7 +23263,7 @@ getGlobalTensorClass().prototype.as2D = function(rows, columns) {
   return reshape(this, [rows, columns]);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23285,7 +23285,7 @@ getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {
   return reshape(this, [rows, columns, depth]);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23307,7 +23307,7 @@ getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {
   return reshape(this, [rows, columns, depth, depth2]);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23329,7 +23329,7 @@ getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, d
   return reshape(this, [rows, columns, depth, depth2, depth3]);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23351,7 +23351,7 @@ getGlobalTensorClass().prototype.asin = function() {
   return asin(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23373,7 +23373,7 @@ getGlobalTensorClass().prototype.asinh = function() {
   return asinh(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23395,7 +23395,7 @@ getGlobalTensorClass().prototype.atan = function() {
   return atan(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23417,7 +23417,7 @@ getGlobalTensorClass().prototype.atan2 = function(b) {
   return atan2(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23439,7 +23439,7 @@ getGlobalTensorClass().prototype.atanh = function() {
   return atanh(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23461,7 +23461,7 @@ getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, d
   return avgPool(this, filterSize, strides, pad3, dimRoundingMode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23483,7 +23483,7 @@ getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {
   return batchToSpaceND(this, blockShape, crops);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23505,7 +23505,7 @@ getGlobalTensorClass().prototype.batchNorm = function(mean4, variance, offset, s
   return batchNorm(this, mean4, variance, offset, scale2, varianceEpsilon);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23527,7 +23527,7 @@ getGlobalTensorClass().prototype.broadcastTo = function(shape) {
   return broadcastTo(this, shape);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23549,7 +23549,7 @@ getGlobalTensorClass().prototype.cast = function(dtype) {
   return cast(this, dtype);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23571,7 +23571,7 @@ getGlobalTensorClass().prototype.ceil = function() {
   return ceil(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23593,7 +23593,7 @@ getGlobalTensorClass().prototype.clipByValue = function(min6, max6) {
   return clipByValue(this, min6, max6);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23618,7 +23618,7 @@ getGlobalTensorClass().prototype.concat = function(x, axis) {
   return concat([this, ...x], axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23640,7 +23640,7 @@ getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFor
   return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23662,7 +23662,7 @@ getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape,
   return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23684,7 +23684,7 @@ getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFo
   return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23706,7 +23706,7 @@ getGlobalTensorClass().prototype.cos = function() {
   return cos(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23728,7 +23728,7 @@ getGlobalTensorClass().prototype.cosh = function() {
   return cosh(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23750,7 +23750,7 @@ getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {
   return cumsum(this, axis, exclusive, reverse5);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23772,7 +23772,7 @@ getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat)
   return depthToSpace(this, blockSize, dataFormat);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23794,7 +23794,7 @@ getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad
   return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23816,7 +23816,7 @@ getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, di
   return dilation2d(this, filter, strides, pad3, dilations, dataFormat);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23838,7 +23838,7 @@ getGlobalTensorClass().prototype.divNoNan = function(b) {
   return divNoNan(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23860,7 +23860,7 @@ getGlobalTensorClass().prototype.div = function(b) {
   return div(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23882,7 +23882,7 @@ getGlobalTensorClass().prototype.dot = function(b) {
   return dot(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23904,7 +23904,7 @@ getGlobalTensorClass().prototype.elu = function() {
   return elu(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23926,7 +23926,7 @@ getGlobalTensorClass().prototype.equal = function(b) {
   return equal(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23948,7 +23948,7 @@ getGlobalTensorClass().prototype.erf = function() {
   return erf(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23970,7 +23970,7 @@ getGlobalTensorClass().prototype.exp = function() {
   return exp(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -23992,7 +23992,7 @@ getGlobalTensorClass().prototype.expandDims = function(axis) {
   return expandDims(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24014,7 +24014,7 @@ getGlobalTensorClass().prototype.expm1 = function() {
   return expm1(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24036,7 +24036,7 @@ getGlobalTensorClass().prototype.fft = function() {
   return fft(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24058,7 +24058,7 @@ getGlobalTensorClass().prototype.flatten = function() {
   return reshape(this, [this.size]);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24080,7 +24080,7 @@ getGlobalTensorClass().prototype.floor = function() {
   return floor(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24102,7 +24102,7 @@ getGlobalTensorClass().prototype.floorDiv = function(b) {
   return floorDiv(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24124,7 +24124,7 @@ getGlobalTensorClass().prototype.gather = function(indices, axis) {
   return gather(this, indices, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24146,7 +24146,7 @@ getGlobalTensorClass().prototype.greaterEqual = function(b) {
   return greaterEqual(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24168,7 +24168,7 @@ getGlobalTensorClass().prototype.greater = function(b) {
   return greater(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24190,7 +24190,7 @@ getGlobalTensorClass().prototype.ifft = function() {
   return ifft(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24212,7 +24212,7 @@ getGlobalTensorClass().prototype.irfft = function() {
   return irfft(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24234,7 +24234,7 @@ getGlobalTensorClass().prototype.isFinite = function() {
   return isFinite2(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24256,7 +24256,7 @@ getGlobalTensorClass().prototype.isInf = function() {
   return isInf(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24278,7 +24278,7 @@ getGlobalTensorClass().prototype.isNaN = function() {
   return isNaN2(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24300,7 +24300,7 @@ getGlobalTensorClass().prototype.leakyRelu = function(alpha) {
   return leakyRelu(this, alpha);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24322,7 +24322,7 @@ getGlobalTensorClass().prototype.lessEqual = function(b) {
   return lessEqual(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24344,7 +24344,7 @@ getGlobalTensorClass().prototype.less = function(b) {
   return less(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24366,7 +24366,7 @@ getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadi
   return localResponseNormalization(this, depthRadius, bias, alpha, beta);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24388,7 +24388,7 @@ getGlobalTensorClass().prototype.logSigmoid = function() {
   return logSigmoid(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24410,7 +24410,7 @@ getGlobalTensorClass().prototype.logSoftmax = function(axis) {
   return logSoftmax(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24432,7 +24432,7 @@ getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {
   return logSumExp(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24454,7 +24454,7 @@ getGlobalTensorClass().prototype.log = function() {
   return log(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24476,7 +24476,7 @@ getGlobalTensorClass().prototype.log1p = function() {
   return log1p(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24498,7 +24498,7 @@ getGlobalTensorClass().prototype.logicalAnd = function(b) {
   return logicalAnd(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24520,7 +24520,7 @@ getGlobalTensorClass().prototype.logicalNot = function() {
   return logicalNot(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24542,7 +24542,7 @@ getGlobalTensorClass().prototype.logicalOr = function(b) {
   return logicalOr(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24564,7 +24564,7 @@ getGlobalTensorClass().prototype.logicalXor = function(b) {
   return logicalXor(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24586,7 +24586,7 @@ getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {
   return matMul(this, b, transposeA, transposeB);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24608,7 +24608,7 @@ getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, d
   return maxPool(this, filterSize, strides, pad3, dimRoundingMode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24630,7 +24630,7 @@ getGlobalTensorClass().prototype.max = function(axis, keepDims) {
   return max(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24652,7 +24652,7 @@ getGlobalTensorClass().prototype.maximum = function(b) {
   return maximum(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24674,7 +24674,7 @@ getGlobalTensorClass().prototype.mean = function(axis, keepDims) {
   return mean(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24696,7 +24696,7 @@ getGlobalTensorClass().prototype.min = function(axis, keepDims) {
   return min(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24718,7 +24718,7 @@ getGlobalTensorClass().prototype.minimum = function(b) {
   return minimum(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24740,7 +24740,7 @@ getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {
   return mirrorPad(this, paddings, mode);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24762,7 +24762,7 @@ getGlobalTensorClass().prototype.mod = function(b) {
   return mod(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24784,7 +24784,7 @@ getGlobalTensorClass().prototype.mul = function(b) {
   return mul(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24806,7 +24806,7 @@ getGlobalTensorClass().prototype.neg = function() {
   return neg(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24828,7 +24828,7 @@ getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {
   return norm(this, ord, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24850,7 +24850,7 @@ getGlobalTensorClass().prototype.notEqual = function(b) {
   return notEqual(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24872,7 +24872,7 @@ getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue
   return oneHot(this, depth, onValue, offValue);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24894,7 +24894,7 @@ getGlobalTensorClass().prototype.onesLike = function() {
   return onesLike(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24916,7 +24916,7 @@ getGlobalTensorClass().prototype.pad = function(paddings, constantValue) {
   return pad(this, paddings, constantValue);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24938,7 +24938,7 @@ getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, paddi
   return pool(this, windowShape, poolingType, padding, dilationRate, strides);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24960,7 +24960,7 @@ getGlobalTensorClass().prototype.pow = function(exp4) {
   return pow(this, exp4);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -24982,7 +24982,7 @@ getGlobalTensorClass().prototype.prelu = function(alpha) {
   return prelu(this, alpha);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25004,7 +25004,7 @@ getGlobalTensorClass().prototype.prod = function(axis, keepDims) {
   return prod(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25026,7 +25026,7 @@ getGlobalTensorClass().prototype.reciprocal = function() {
   return reciprocal(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25048,7 +25048,7 @@ getGlobalTensorClass().prototype.relu = function() {
   return relu(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25070,7 +25070,7 @@ getGlobalTensorClass().prototype.relu6 = function() {
   return relu6(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25092,7 +25092,7 @@ getGlobalTensorClass().prototype.reshapeAs = function(x) {
   return reshape(this, x.shape);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25114,7 +25114,7 @@ getGlobalTensorClass().prototype.reshape = function(shape) {
   return reshape(this, shape);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25136,7 +25136,7 @@ getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorn
   return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25158,7 +25158,7 @@ getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, al
   return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25180,7 +25180,7 @@ getGlobalTensorClass().prototype.reverse = function(axis) {
   return reverse(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25202,7 +25202,7 @@ getGlobalTensorClass().prototype.rfft = function() {
   return rfft(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25224,7 +25224,7 @@ getGlobalTensorClass().prototype.round = function() {
   return round2(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25246,7 +25246,7 @@ getGlobalTensorClass().prototype.rsqrt = function() {
   return rsqrt(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25268,7 +25268,7 @@ getGlobalTensorClass().prototype.selu = function() {
   return selu(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25290,7 +25290,7 @@ getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, poi
   return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25312,7 +25312,7 @@ getGlobalTensorClass().prototype.sigmoid = function() {
   return sigmoid(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25334,7 +25334,7 @@ getGlobalTensorClass().prototype.sign = function() {
   return sign(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25356,7 +25356,7 @@ getGlobalTensorClass().prototype.sin = function() {
   return sin(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25378,7 +25378,7 @@ getGlobalTensorClass().prototype.sinh = function() {
   return sinh(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25400,7 +25400,7 @@ getGlobalTensorClass().prototype.slice = function(begin, size) {
   return slice(this, begin, size);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25422,7 +25422,7 @@ getGlobalTensorClass().prototype.softmax = function(dim) {
   return softmax(this, dim);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25444,7 +25444,7 @@ getGlobalTensorClass().prototype.softplus = function() {
   return softplus(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25466,7 +25466,7 @@ getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings)
   return spaceToBatchND(this, blockShape, paddings);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25488,7 +25488,7 @@ getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {
   return split(this, numOrSizeSplits, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25510,7 +25510,7 @@ getGlobalTensorClass().prototype.sqrt = function() {
   return sqrt(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25532,7 +25532,7 @@ getGlobalTensorClass().prototype.square = function() {
   return square(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25554,7 +25554,7 @@ getGlobalTensorClass().prototype.squaredDifference = function(b) {
   return squaredDifference(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25576,7 +25576,7 @@ getGlobalTensorClass().prototype.squeeze = function(axis) {
   return squeeze(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25599,7 +25599,7 @@ getGlobalTensorClass().prototype.stack = function(x, axis) {
   return stack(tensorsToBeStacked, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25621,7 +25621,7 @@ getGlobalTensorClass().prototype.step = function(alpha) {
   return step(this, alpha);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25643,7 +25643,7 @@ getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, be
   return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25665,7 +25665,7 @@ getGlobalTensorClass().prototype.sub = function(b) {
   return sub(this, b);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25687,7 +25687,7 @@ getGlobalTensorClass().prototype.sum = function(axis, keepDims) {
   return sum2(this, axis, keepDims);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25709,7 +25709,7 @@ getGlobalTensorClass().prototype.tan = function() {
   return tan(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25731,7 +25731,7 @@ getGlobalTensorClass().prototype.tanh = function() {
   return tanh2(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25753,7 +25753,7 @@ getGlobalTensorClass().prototype.tile = function(reps) {
   return tile(this, reps);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25775,7 +25775,7 @@ getGlobalTensorClass().prototype.toBool = function() {
   return cast(this, "bool");
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25797,7 +25797,7 @@ getGlobalTensorClass().prototype.toFloat = function() {
   return cast(this, "float32");
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25819,7 +25819,7 @@ getGlobalTensorClass().prototype.toInt = function() {
   return cast(this, "int32");
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25841,7 +25841,7 @@ getGlobalTensorClass().prototype.topk = function(k, sorted) {
   return topk(this, k, sorted);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25863,7 +25863,7 @@ getGlobalTensorClass().prototype.transpose = function(perm) {
   return transpose(this, perm);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25885,7 +25885,7 @@ getGlobalTensorClass().prototype.unique = function(axis) {
   return unique(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25907,7 +25907,7 @@ getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSe
   return unsortedSegmentSum(this, segmentIds, numSegments);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25929,7 +25929,7 @@ getGlobalTensorClass().prototype.unstack = function(axis) {
   return unstack(this, axis);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25951,7 +25951,7 @@ getGlobalTensorClass().prototype.where = function(condition, x) {
   return where(condition, this, x);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25973,7 +25973,7 @@ getGlobalTensorClass().prototype.zerosLike = function() {
   return zerosLike(this);
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops.js
+// node_modules/.pnpm/@tensorflow#tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -25991,7 +25991,7 @@ getGlobalTensorClass().prototype.zerosLike = function() {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
 var exports_constraints_exports = {};
 __export(exports_constraints_exports, {
   maxNorm: () => maxNorm,
@@ -26000,7 +26000,7 @@ __export(exports_constraints_exports, {
   unitNorm: () => unitNorm
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26021,7 +26021,7 @@ function imageDataFormat() {
   return "channelsLast";
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/errors.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/errors.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26062,7 +26062,7 @@ var AssertionError = class extends Error {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26311,7 +26311,7 @@ function mapActivationToFusedKernel(activationName) {
   return null;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/constraints.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/constraints.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26429,7 +26429,7 @@ function getConstraint(identifier) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26452,7 +26452,7 @@ function minMaxNorm(config) {
   return new MinMaxNorm(config);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
 var exports_initializers_exports = {};
 __export(exports_initializers_exports, {
   constant: () => constant,
@@ -26472,7 +26472,7 @@ __export(exports_initializers_exports, {
   zeros: () => zeros2
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26488,7 +26488,7 @@ var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"];
 var VALID_POOL_MODE_VALUES = ["max", "avg"];
 var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"];
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/common.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/common.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26559,7 +26559,7 @@ function isValidTensorName(name) {
   return !!name.match(tensorNameRegex);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26606,7 +26606,7 @@ function range2(begin, end) {
   return out;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26928,7 +26928,7 @@ function inTrainPhase(x, alt, training = false) {
   return training ? x() : alt();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -26941,7 +26941,7 @@ function inTrainPhase(x, alt, training = false) {
 var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"];
 var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"];
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/initializers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/initializers.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -27332,7 +27332,7 @@ function getInitializer(identifier) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -27388,7 +27388,7 @@ function orthogonal(args) {
   return new Orthogonal(args);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
 var exports_layers_exports = {};
 __export(exports_layers_exports, {
   Layer: () => Layer,
@@ -27467,7 +27467,7 @@ __export(exports_layers_exports, {
   zeroPadding2d: () => zeroPadding2d
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -27490,7 +27490,7 @@ function getUid(prefix = "") {
   return prefix + _uidPrefixes[prefix].toString();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -27537,7 +27537,7 @@ function getExactlyOneShape(shapes) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -27559,7 +27559,7 @@ function countParamsInWeights(weights) {
   return count2;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/variables.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/variables.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -27629,7 +27629,7 @@ function batchSetValue(variablesAndValues) {
   });
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -28237,7 +28237,7 @@ function getSourceInputs(tensor2, layer, nodeIndex) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -28339,7 +28339,7 @@ function Input(config) {
   return outputs[0];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/logs.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/logs.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -28385,7 +28385,7 @@ function disposeTensorsInLogs(logs) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -28743,7 +28743,7 @@ function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainS
   return {callbackList, history};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -28757,7 +28757,7 @@ function deserialize(config, customObjects = {}, fastWeightInit = false) {
   return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/losses.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/losses.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -28919,7 +28919,7 @@ function get(identifierOrFn) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/metrics.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/metrics.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -29043,7 +29043,7 @@ function getLossOrMetricName(fn) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -29074,7 +29074,7 @@ function getOptimizer(identifier) {
   throw new ValueError(`Unknown Optimizer ${identifier}`);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js
 /**
  * @license
  * Copyright 2019 Google LLC
@@ -29129,7 +29129,7 @@ function plainObjectCheck(x) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -29281,7 +29281,7 @@ function printLayerSummaryWithConnections(layer, positions, relevantNodes, print
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -29361,11 +29361,11 @@ function convertTsToPythonic(tsConfig, key) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/version.js
 /** @license See the LICENSE file. */
 var version2 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -29657,7 +29657,7 @@ function getNodeOutputs(fetch3) {
   return layerOutputs;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -30389,7 +30389,7 @@ var Container = class extends Layer {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -30475,7 +30475,7 @@ function computeWeightedLoss2(losses4, sampleWeights) {
   return mul(losses4, sampleWeights);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -30715,7 +30715,7 @@ async function evaluateDataset(model2, dataset, args) {
   return singletonOrArray(outs);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -30993,7 +30993,7 @@ function disposeNewTensors(tensors, refTensors) {
   });
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -31860,7 +31860,7 @@ var Functional = class extends LayersModel {
 Functional.className = "Functional";
 serialization_exports.registerClass(Functional);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/models.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/models.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -32213,7 +32213,7 @@ var Sequential = class extends LayersModel {
 Sequential.className = "Sequential";
 serialization_exports.registerClass(Sequential);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -32242,7 +32242,7 @@ function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
   CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/activations.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/activations.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -32373,7 +32373,7 @@ function getActivation(identifier) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -32452,7 +32452,7 @@ function getRegularizer(identifier) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -32655,7 +32655,7 @@ var Softmax3 = class extends Layer {
 Softmax3.className = "Softmax";
 serialization_exports.registerClass(Softmax3);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -32708,7 +32708,7 @@ function deconvLength(dimSize, strideSize, kernelSize, padding) {
   return dimSize;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -33335,7 +33335,7 @@ var UpSampling2D = class extends Layer {
 UpSampling2D.className = "UpSampling2D";
 serialization_exports.registerClass(UpSampling2D);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -33436,7 +33436,7 @@ var DepthwiseConv2D = class extends BaseConv {
 DepthwiseConv2D.className = "DepthwiseConv2D";
 serialization_exports.registerClass(DepthwiseConv2D);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -34468,7 +34468,7 @@ function generateDropoutMask(args) {
   return masks.map((m) => keep(m.clone()));
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
 /**
  * @license
  * Copyright 2020 Google LLC
@@ -34760,7 +34760,7 @@ var ConvLSTM2D = class extends ConvRNN2D {
 ConvLSTM2D.className = "ConvLSTM2D";
 serialization_exports.registerClass(ConvLSTM2D);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -35164,7 +35164,7 @@ var Masking = class extends Layer {
 Masking.className = "Masking";
 serialization_exports.registerClass(Masking);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -35271,7 +35271,7 @@ var Embedding = class extends Layer {
 Embedding.className = "Embedding";
 serialization_exports.registerClass(Embedding);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -35811,7 +35811,7 @@ var Dot = class extends Merge {
 Dot.className = "Dot";
 serialization_exports.registerClass(Dot);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -35924,7 +35924,7 @@ var AlphaDropout = class extends Layer {
 AlphaDropout.className = "AlphaDropout";
 serialization_exports.registerClass(AlphaDropout);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -36211,7 +36211,7 @@ var LayerNormalization = class extends Layer {
 LayerNormalization.className = "LayerNormalization";
 serialization_exports.registerClass(LayerNormalization);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -36329,7 +36329,7 @@ var ZeroPadding2D = class extends Layer {
 ZeroPadding2D.className = "ZeroPadding2D";
 serialization_exports.registerClass(ZeroPadding2D);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -36731,7 +36731,7 @@ var GlobalMaxPooling2D = class extends GlobalPooling2D {
 GlobalMaxPooling2D.className = "GlobalMaxPooling2D";
 serialization_exports.registerClass(GlobalMaxPooling2D);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -37109,7 +37109,7 @@ var Bidirectional = class extends Wrapper {
 Bidirectional.className = "Bidirectional";
 serialization_exports.registerClass(Bidirectional);
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -37322,7 +37322,7 @@ function masking(args) {
   return new Masking(args);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js
 var exports_metrics_exports = {};
 __export(exports_metrics_exports, {
   MAPE: () => MAPE2,
@@ -37387,7 +37387,7 @@ function mse2(yTrue, yPred) {
   return meanSquaredError2(yTrue, yPred);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js
 var exports_models_exports = {};
 __export(exports_models_exports, {
   modelFromJSON: () => modelFromJSON
@@ -37402,7 +37402,7 @@ __export(exports_models_exports, {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js
 var exports_regularizers_exports = {};
 __export(exports_regularizers_exports, {
   l1: () => l12,
@@ -37428,7 +37428,7 @@ function l22(config) {
   return l2(config);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -37537,7 +37537,7 @@ function earlyStopping(args) {
 }
 var callbacks = {earlyStopping};
 
-// node_modules/.pnpm/@tensorflow/tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-layers@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-layers/dist/index.js
 /**
  * @license
  * Copyright 2018 Google LLC
@@ -37548,7 +37548,7 @@ var callbacks = {earlyStopping};
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -37608,7 +37608,7 @@ var SaverDef;
   })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));
 })(SaverDef || (SaverDef = {}));
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -37643,7 +37643,7 @@ function deregisterOp(name) {
   delete CUSTOM_OPS[name];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -37730,7 +37730,7 @@ function cloneTensor(tensor2) {
   return tensor2.kept ? tensor2 : clone(tensor2);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js
 var arithmetic_exports = {};
 __export(arithmetic_exports, {
   json: () => json
@@ -37933,7 +37933,7 @@ var json = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js
 var basic_math_exports = {};
 __export(basic_math_exports, {
   json: () => json2
@@ -38412,7 +38412,7 @@ var json2 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js
 var control_exports = {};
 __export(control_exports, {
   json: () => json3
@@ -38759,7 +38759,7 @@ var json3 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js
 var convolution_exports = {};
 __export(convolution_exports, {
   json: () => json4
@@ -39144,7 +39144,7 @@ var json4 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js
 var creation_exports = {};
 __export(creation_exports, {
   json: () => json5
@@ -39326,7 +39326,7 @@ var json5 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js
 var dynamic_exports = {};
 __export(dynamic_exports, {
   json: () => json6
@@ -39432,7 +39432,7 @@ var json6 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js
 var evaluation_exports = {};
 __export(evaluation_exports, {
   json: () => json7
@@ -39480,7 +39480,7 @@ var json7 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js
 var graph_exports = {};
 __export(graph_exports, {
   json: () => json8
@@ -39599,7 +39599,7 @@ var json8 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js
 var hash_table_exports = {};
 __export(hash_table_exports, {
   json: () => json9
@@ -39723,7 +39723,7 @@ var json9 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js
 var image_exports = {};
 __export(image_exports, {
   json: () => json10
@@ -39799,7 +39799,7 @@ var json10 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js
 var logical_exports = {};
 __export(logical_exports, {
   json: () => json11
@@ -39948,7 +39948,7 @@ var json11 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js
 var matrices_exports = {};
 __export(matrices_exports, {
   json: () => json12
@@ -40092,7 +40092,7 @@ var json12 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js
 var normalization_exports = {};
 __export(normalization_exports, {
   json: () => json13
@@ -40246,7 +40246,7 @@ var json13 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js
 var reduction_exports = {};
 __export(reduction_exports, {
   json: () => json14
@@ -40380,7 +40380,7 @@ var json14 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js
 var slice_join_exports = {};
 __export(slice_join_exports, {
   json: () => json15
@@ -40610,7 +40610,7 @@ var json15 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js
 var spectral_exports = {};
 __export(spectral_exports, {
   json: () => json16
@@ -40670,7 +40670,7 @@ var json16 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js
 var transformation_exports = {};
 __export(transformation_exports, {
   json: () => json17
@@ -40814,7 +40814,7 @@ var json17 = [
   }
 ];
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -41247,7 +41247,7 @@ function getBoolArrayParam(attrs, name, def) {
   return def;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -41323,7 +41323,7 @@ var NodeValueImpl = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -41385,7 +41385,7 @@ var executeOp = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -41502,7 +41502,7 @@ var executeOp2 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -41574,7 +41574,7 @@ function mergeElementShape(elementShapeA, elementShapeB) {
   return result;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -41761,7 +41761,7 @@ var TensorArray = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -41979,7 +41979,7 @@ function split2(tensor2, length, elementShape) {
   return list;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42245,7 +42245,7 @@ var executeOp3 = async (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42411,7 +42411,7 @@ var executeOp4 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42488,7 +42488,7 @@ var executeOp5 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42553,7 +42553,7 @@ var executeOp6 = async (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42595,7 +42595,7 @@ var executeOp7 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42659,7 +42659,7 @@ var executeOp8 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -42744,7 +42744,7 @@ var HashTable = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -42798,7 +42798,7 @@ var executeOp9 = async (node, tensorMap, context, resourceManager) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42845,7 +42845,7 @@ var executeOp10 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42900,7 +42900,7 @@ var executeOp11 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42955,7 +42955,7 @@ var executeOp12 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -42998,7 +42998,7 @@ var executeOp13 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -43083,7 +43083,7 @@ var executeOp14 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -43210,7 +43210,7 @@ var executeOp15 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -43246,7 +43246,7 @@ var executeOp16 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -43309,7 +43309,7 @@ var executeOp17 = (node, tensorMap, context) => {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -43380,7 +43380,7 @@ function executeOp18(node, tensorMap, context, resourceManager) {
   return [].concat(value);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js
 var ExecutionContext = class {
   constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {
     this.weightMap = weightMap;
@@ -43477,7 +43477,7 @@ var ExecutionContext = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -43613,7 +43613,7 @@ function isHashTable(node) {
   return HASH_TABLE_OPS.indexOf(node.op) >= 0;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -43992,7 +43992,7 @@ var GraphExecutor = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js
 var ResourceManager = class {
   constructor(hashTableNameToHandle = {}, hashTableMap = {}) {
     this.hashTableNameToHandle = hashTableNameToHandle;
@@ -44020,7 +44020,7 @@ var ResourceManager = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -44202,11 +44202,11 @@ async function loadGraphModel(modelUrl, options = {}) {
   return model2;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/version.js
 /** @license See the LICENSE file. */
 var version3 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-converter@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-converter/dist/index.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -44224,7 +44224,7 @@ var version3 = "3.3.0";
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/index.js
 var dist_exports = {};
 __export(dist_exports, {
   CSVDataset: () => CSVDataset,
@@ -44242,13 +44242,13 @@ __export(dist_exports, {
   zip: () => zip
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/dataset.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/dataset.js
 var seedrandom3 = __toModule(require_seedrandom4());
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
 var seedrandom2 = __toModule(require_seedrandom4());
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -44361,7 +44361,7 @@ function isPrimitive(value) {
   return value === null || typeof value !== "object" && typeof value !== "function";
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -44392,7 +44392,7 @@ function cloneIfTensor(item) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -44503,7 +44503,7 @@ var RingBuffer = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -44556,7 +44556,7 @@ var GrowingRingBuffer = class extends RingBuffer {
 };
 GrowingRingBuffer.INITIAL_CAPACITY = 32;
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -45102,7 +45102,7 @@ var ShuffleIterator = class extends PrefetchIterator {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/dataset.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/dataset.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -45322,7 +45322,7 @@ function batchConcat(arrays) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -45358,7 +45358,7 @@ var TextLineDataset = class extends Dataset {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -45610,7 +45610,7 @@ var CSVDataset = class extends Dataset {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -45764,7 +45764,7 @@ var MicrophoneIterator = class extends LazyIterator {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -45915,7 +45915,7 @@ var WebcamIterator = class extends LazyIterator {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/datasource.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/datasource.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -45936,7 +45936,7 @@ var WebcamIterator = class extends LazyIterator {
 var DataSource = class {
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46002,7 +46002,7 @@ var SplitIteratorImpl = class extends OneToManyIterator {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46071,7 +46071,7 @@ var Utf8IteratorImpl = class extends OneToManyIterator {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46136,7 +46136,7 @@ var FileChunkIterator = class extends ByteChunkIterator {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46186,7 +46186,7 @@ var getRequestInitFromRequest = (request) => {
   return init2;
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46208,7 +46208,7 @@ function isLocalPath(source) {
   return typeof source === "string" && source.substr(0, 7) === "file://";
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46241,7 +46241,7 @@ var FileDataSource = class extends DataSource {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46274,7 +46274,7 @@ var URLDataSource = class extends DataSource {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/readers.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/readers.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46312,11 +46312,11 @@ async function microphone(microphoneConfig) {
   return MicrophoneIterator.create(microphoneConfig);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/version.js
 /** @license See the LICENSE file. */
 var version4 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-data@3.3.0_67c79a722ac85e5c2aacae49a2e61dcd/node_modules/@tensorflow/tfjs-data/dist/index.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -46334,7 +46334,7 @@ var version4 = "3.3.0";
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -46362,7 +46362,7 @@ function assertNotComplex(tensor2, opName) {
   });
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -46508,7 +46508,7 @@ var MathBackendCPU = class extends KernelBackend {
 };
 MathBackendCPU.nextDataId = 0;
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
 var shared_exports = {};
 __export(shared_exports, {
   addImpl: () => addImpl,
@@ -46544,7 +46544,7 @@ __export(shared_exports, {
   uniqueImpl: () => uniqueImpl
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46583,7 +46583,7 @@ var absConfig = {
   kernelFunc: abs2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46633,7 +46633,7 @@ function createSimpleBinaryKernelImpl(op2) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46669,7 +46669,7 @@ var complexConfig = {
   kernelFunc: complex2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46696,7 +46696,7 @@ function zeros3(backend2, shape, dtype = "float32") {
   return backend2.makeTensorInfo(shape, dtype, values);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46725,7 +46725,7 @@ var identityConfig = {
   kernelFunc: identity2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46755,7 +46755,7 @@ var realConfig = {
   kernelFunc: real2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46816,7 +46816,7 @@ var castConfig = {
   kernelFunc: cast3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46922,7 +46922,7 @@ function createComplexBinaryKernelImpl(op2) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -46950,7 +46950,7 @@ var addConfig = {
   kernelFunc: add4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47013,7 +47013,7 @@ function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
   return outBuf;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47040,7 +47040,7 @@ function createSimpleUnaryImpl(op2) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47090,7 +47090,7 @@ function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47115,7 +47115,7 @@ var ceilConfig = {
   kernelFunc: ceil2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47158,7 +47158,7 @@ function concatImpl(inputs, outShape, dtype, simplyConcat) {
   return outVals;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47183,7 +47183,7 @@ var expConfig = {
   kernelFunc: exp2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47208,7 +47208,7 @@ var expm1Config = {
   kernelFunc: expm12
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47233,7 +47233,7 @@ var floorConfig = {
   kernelFunc: floor2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47265,7 +47265,7 @@ function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
   return outBuf;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47290,7 +47290,7 @@ var greaterConfig = {
   kernelFunc: greater3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47315,7 +47315,7 @@ var lessConfig = {
   kernelFunc: less3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47342,7 +47342,7 @@ function linSpaceImpl(start, stop, num) {
   return values;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47367,7 +47367,7 @@ var logConfig = {
   kernelFunc: log3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47400,7 +47400,7 @@ function maxImpl(aVals, reduceSize, outShape, dtype) {
   return vals;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47425,7 +47425,7 @@ var maximumConfig = {
   kernelFunc: maximum3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47450,7 +47450,7 @@ var minimumConfig = {
   kernelFunc: minimum3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47481,7 +47481,7 @@ var multiplyConfig = {
   kernelFunc: multiply2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47516,7 +47516,7 @@ var negConfig = {
   kernelFunc: neg2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47541,7 +47541,7 @@ var notEqualConfig = {
   kernelFunc: notEqual2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47576,7 +47576,7 @@ function transposeImpl(xVals, xShape, dtype, perm, newShape) {
   return result;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47614,7 +47614,7 @@ var transposeConfig = {
   kernelFunc: transpose2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47677,7 +47677,7 @@ var prodConfig = {
   kernelFunc: prod2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47713,7 +47713,7 @@ function rangeImpl(start, stop, step5, dtype) {
   return values;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47738,7 +47738,7 @@ var rsqrtConfig = {
   kernelFunc: rsqrt2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47796,7 +47796,7 @@ var sliceConfig = {
   kernelFunc: slice2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47824,7 +47824,7 @@ var squaredDifferenceConfig = {
   kernelFunc: squaredDifference2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47854,7 +47854,7 @@ function stridedSliceImpl(outShape, xBuf, strides, begin) {
   return outBuf;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47882,7 +47882,7 @@ var subConfig = {
   kernelFunc: sub2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -47917,7 +47917,7 @@ function tileImpl(xBuf, reps) {
   return result;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -47963,7 +47963,7 @@ function topKImpl(x, xShape, xDtype, k, sorted) {
   ];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48036,7 +48036,7 @@ function uniqueImpl(values, axis, shape, dtype) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48054,11 +48054,11 @@ function uniqueImpl(values, axis, shape, dtype) {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/version.js
 /** @license See the LICENSE file. */
 var version5 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48077,7 +48077,7 @@ var version5 = "3.3.0";
  */
 registerBackend("cpu", () => new MathBackendCPU(), 1);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48101,7 +48101,7 @@ var eluConfig = {
   kernelFunc: elu4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48137,7 +48137,7 @@ var leakyReluConfig = {
   kernelFunc: leakyRelu2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48170,7 +48170,7 @@ var preluConfig = {
   kernelFunc: prelu3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48194,7 +48194,7 @@ var reluConfig = {
   kernelFunc: relu2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48218,7 +48218,7 @@ var relu6Config = {
   kernelFunc: relu62
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48252,7 +48252,7 @@ function applyActivation2(backend2, x, activation2, preluActivationWeights, leak
   throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48293,7 +48293,7 @@ var reshapeConfig = {
   kernelFunc: reshape3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48382,7 +48382,7 @@ var batchMatMulConfig = {
   kernelFunc: batchMatMul
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48430,7 +48430,7 @@ var _fusedMatMulConfig = {
   kernelFunc: _fusedMatMul
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48454,7 +48454,7 @@ var acosConfig = {
   kernelFunc: acos2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48478,7 +48478,7 @@ var acoshConfig = {
   kernelFunc: acosh2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48516,7 +48516,7 @@ var addNConfig = {
   kernelFunc: addN2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48578,7 +48578,7 @@ var allConfig = {
   kernelFunc: all2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48640,7 +48640,7 @@ var anyConfig = {
   kernelFunc: any2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48700,7 +48700,7 @@ var argMaxConfig = {
   kernelFunc: argMax2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48760,7 +48760,7 @@ var argMinConfig = {
   kernelFunc: argMin2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48784,7 +48784,7 @@ var asinConfig = {
   kernelFunc: asin2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48808,7 +48808,7 @@ var asinhConfig = {
   kernelFunc: asinh2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48832,7 +48832,7 @@ var atanConfig = {
   kernelFunc: atan3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48857,7 +48857,7 @@ var atan2Config = {
   kernelFunc: atan22
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -48881,7 +48881,7 @@ var atanhConfig = {
   kernelFunc: atanh2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49151,7 +49151,7 @@ function maxPool3dPositions(xBuf, convInfo) {
   return maxPositions;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49193,7 +49193,7 @@ var avgPoolConfig = {
   kernelFunc: avgPool2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49226,7 +49226,7 @@ var avgPool3DConfig = {
   kernelFunc: avgPool3D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49310,7 +49310,7 @@ var avgPool3DGradConfig2 = {
   kernelFunc: avgPool3DGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49382,7 +49382,7 @@ var avgPoolGradConfig2 = {
   kernelFunc: avgPoolGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49447,7 +49447,7 @@ var batchNormConfig = {
   kernelFunc: batchNorm2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49494,7 +49494,7 @@ var batchToSpaceNDConfig = {
   kernelFunc: batchToSpaceND2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49526,7 +49526,7 @@ var bincountConfig = {
   kernelFunc: bincount2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Clip.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Clip.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49556,7 +49556,7 @@ var clipConfig = {
   kernelFunc: clip
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49595,7 +49595,7 @@ var complexAbsConfig = {
   kernelFunc: complexAbs
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49625,7 +49625,7 @@ var imagConfig = {
   kernelFunc: imag2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49690,7 +49690,7 @@ var concatConfig = {
   kernelFunc: concat2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49779,7 +49779,7 @@ var conv2DConfig = {
   kernelFunc: conv2D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49847,7 +49847,7 @@ var conv2DBackpropFilterConfig = {
   kernelFunc: conv2DBackpropFilter2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -49929,7 +49929,7 @@ var conv2DBackpropInputConfig = {
   kernelFunc: conv2DBackpropInput2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50018,7 +50018,7 @@ var conv3DConfig = {
   kernelFunc: conv3D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50109,7 +50109,7 @@ var conv3DBackpropFilterV2Config = {
   kernelFunc: conv3DBackpropFilterV2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50190,7 +50190,7 @@ var conv3DBackpropInputV2Config = {
   kernelFunc: conv3DBackpropInputV2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50214,7 +50214,7 @@ var cosConfig = {
   kernelFunc: cos2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50238,7 +50238,7 @@ var coshConfig = {
   kernelFunc: cosh2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50351,7 +50351,7 @@ var cropAndResizeConfig = {
   kernelFunc: cropAndResize2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50414,7 +50414,7 @@ var cumsumConfig = {
   kernelFunc: cumsum2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50454,7 +50454,7 @@ var denseBincountConfig = {
   kernelFunc: denseBincount2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50511,7 +50511,7 @@ var depthToSpaceConfig = {
   kernelFunc: depthToSpace2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50595,7 +50595,7 @@ var depthwiseConv2dNativeConfig = {
   kernelFunc: depthwiseConv2dNative
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50658,7 +50658,7 @@ var depthwiseConv2dNativeBackpropFilterConfig = {
   kernelFunc: depthwiseConv2dNativeBackpropFilter2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50732,7 +50732,7 @@ var depthwiseConv2dNativeBackpropInputConfig = {
   kernelFunc: depthwiseConv2dNativeBackpropInput2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50768,7 +50768,7 @@ var diagConfig = {
   kernelFunc: diag2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50834,7 +50834,7 @@ var dilation2dConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50899,7 +50899,7 @@ var dilation2dBackpropFilterConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -50964,7 +50964,7 @@ var dilation2dBackpropInputConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51004,7 +51004,7 @@ var eluGradConfig2 = {
   kernelFunc: eluGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51029,7 +51029,7 @@ var equalConfig = {
   kernelFunc: equal2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51064,7 +51064,7 @@ var erfConfig = {
   kernelFunc: erf2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51101,7 +51101,7 @@ var expandDimsConfig = {
   kernelFunc: expandDims3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51126,7 +51126,7 @@ var realDivConfig = {
   kernelFunc: div2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51332,7 +51332,7 @@ function fourierTransformByMatmul(data, size, inverse) {
   return ret;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51372,7 +51372,7 @@ var fftConfig = {
   kernelFunc: fft2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51410,7 +51410,7 @@ function fillValues(values, value, dtype) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51463,7 +51463,7 @@ var flipLeftRightConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51488,7 +51488,7 @@ var floorDivConfig = {
   kernelFunc: floorDiv2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51532,7 +51532,7 @@ var fusedConv2DConfig = {
   kernelFunc: fusedConv2D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51576,7 +51576,7 @@ var fusedDepthwiseConv2DConfig = {
   kernelFunc: fusedDepthwiseConv2D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51629,7 +51629,7 @@ var gatherNdConfig = {
   kernelFunc: gatherNd
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51694,7 +51694,7 @@ var gatherV2Config = {
   kernelFunc: gatherV2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51719,7 +51719,7 @@ var greaterEqualConfig = {
   kernelFunc: greaterEqual2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51759,7 +51759,7 @@ var ifftConfig = {
   kernelFunc: ifft2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51783,7 +51783,7 @@ var isFiniteConfig = {
   kernelFunc: isFinite3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51807,7 +51807,7 @@ var isInfConfig = {
   kernelFunc: isInf2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51831,7 +51831,7 @@ var isNaNConfig = {
   kernelFunc: isNaN3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51856,7 +51856,7 @@ var lessEqualConfig = {
   kernelFunc: lessEqual2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51885,7 +51885,7 @@ var linSpaceConfig = {
   kernelFunc: linSpace
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51909,7 +51909,7 @@ var log1pConfig = {
   kernelFunc: log1p2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51934,7 +51934,7 @@ var logicalAndConfig = {
   kernelFunc: logicalAnd2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51958,7 +51958,7 @@ var logicalNotConfig = {
   kernelFunc: logicalNot2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -51983,7 +51983,7 @@ var logicalOrConfig = {
   kernelFunc: logicalOr2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52034,7 +52034,7 @@ var lRNConfig = {
   kernelFunc: lRN
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52089,7 +52089,7 @@ var lRNGradConfig = {
   kernelFunc: lRNGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52145,7 +52145,7 @@ var maxConfig = {
   kernelFunc: max3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52187,7 +52187,7 @@ var maxPoolConfig = {
   kernelFunc: maxPool2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52220,7 +52220,7 @@ var maxPool3DConfig = {
   kernelFunc: maxPool3D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52308,7 +52308,7 @@ var maxPool3DGradConfig2 = {
   kernelFunc: maxPool3DGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52385,7 +52385,7 @@ var maxPoolGradConfig2 = {
   kernelFunc: maxPoolGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52409,7 +52409,7 @@ function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, conv
   return [maxPools.values, maxPositions.values];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52446,7 +52446,7 @@ var maxPoolWithArgmaxConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52516,7 +52516,7 @@ var sumConfig = {
   kernelFunc: sum3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52558,7 +52558,7 @@ var meanConfig = {
   kernelFunc: mean2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52622,7 +52622,7 @@ var minConfig = {
   kernelFunc: min3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52677,7 +52677,7 @@ var mirrorPadConfig = {
   kernelFunc: mirrorPad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52709,10 +52709,10 @@ var modConfig = {
   kernelFunc: mod2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
 var seedrandom4 = __toModule(require_seedrandom2());
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52768,7 +52768,7 @@ var softmaxConfig = {
   kernelFunc: softmax3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52827,7 +52827,7 @@ var multinomialConfig = {
   kernelFunc: multinomial2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52861,7 +52861,7 @@ var nonMaxSuppressionV3Config = {
   kernelFunc: nonMaxSuppressionV3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52898,7 +52898,7 @@ var nonMaxSuppressionV4Config = {
   kernelFunc: nonMaxSuppressionV4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -52939,7 +52939,7 @@ var nonMaxSuppressionV5Config = {
   kernelFunc: nonMaxSuppressionV5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -52978,7 +52978,7 @@ var oneHotConfig = {
   kernelFunc: oneHot2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53021,7 +53021,7 @@ var zerosLikeConfig = {
   kernelFunc: zerosLike2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53064,7 +53064,7 @@ var onesLikeConfig = {
   kernelFunc: onesLike2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53109,7 +53109,7 @@ var packConfig = {
   kernelFunc: pack
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53159,7 +53159,7 @@ var padV2Config = {
   kernelFunc: padV2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53184,7 +53184,7 @@ var powConfig = {
   kernelFunc: pow2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53213,7 +53213,7 @@ var rangeConfig = {
   kernelFunc: range3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53237,7 +53237,7 @@ var reciprocalConfig = {
   kernelFunc: reciprocal2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53323,7 +53323,7 @@ var resizeBilinearConfig = {
   kernelFunc: resizeBilinear2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53403,7 +53403,7 @@ var resizeBilinearGradConfig2 = {
   kernelFunc: resizeBilinearGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53472,7 +53472,7 @@ var resizeNearestNeighborConfig = {
   kernelFunc: resizeNearestNeighbor2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53563,7 +53563,7 @@ var resizeNearestNeighborGradConfig2 = {
   kernelFunc: resizeNearestNeighborGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53606,7 +53606,7 @@ var reverseConfig = {
   kernelFunc: reverse2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53676,7 +53676,7 @@ var rotateWithOffsetConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53713,7 +53713,7 @@ var roundConfig = {
   kernelFunc: round3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53761,7 +53761,7 @@ function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates,
   return outBuf;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53795,7 +53795,7 @@ var scatterNdConfig = {
   kernelFunc: scatterNd
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53841,7 +53841,7 @@ var selectConfig = {
   kernelFunc: select
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53873,7 +53873,7 @@ var seluConfig = {
   kernelFunc: selu2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53897,7 +53897,7 @@ var sigmoidConfig = {
   kernelFunc: sigmoid2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53929,7 +53929,7 @@ var signConfig = {
   kernelFunc: sign2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53953,7 +53953,7 @@ var sinConfig = {
   kernelFunc: sin2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -53977,7 +53977,7 @@ var sinhConfig = {
   kernelFunc: sinh2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54016,7 +54016,7 @@ var softplusConfig = {
   kernelFunc: softplus2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54072,7 +54072,7 @@ var spaceToBatchNDConfig = {
   kernelFunc: spaceToBatchND2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54107,7 +54107,7 @@ var sparseToDenseConfig = {
   kernelFunc: sparseToDense2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54146,7 +54146,7 @@ var splitVConfig = {
   kernelFunc: splitV
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54170,7 +54170,7 @@ var sqrtConfig = {
   kernelFunc: sqrt2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -54205,7 +54205,7 @@ var squareConfig = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54236,7 +54236,7 @@ var stepConfig = {
   kernelFunc: step2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54283,7 +54283,7 @@ var stridedSliceConfig = {
   kernelFunc: stridedSlice2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54307,7 +54307,7 @@ var tanConfig = {
   kernelFunc: tan2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54331,7 +54331,7 @@ var tanhConfig = {
   kernelFunc: tanh3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54362,7 +54362,7 @@ var tileConfig = {
   kernelFunc: tile3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54397,7 +54397,7 @@ var topKConfig = {
   kernelFunc: topK
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -54554,7 +54554,7 @@ function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride,
   return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54589,7 +54589,7 @@ var uniqueConfig = {
   kernelFunc: unique3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54640,7 +54640,7 @@ var unpackConfig = {
   kernelFunc: unpack
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54697,7 +54697,7 @@ var unsortedSegmentSumConfig = {
   kernelFunc: unsortedSegmentSum2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54874,7 +54874,7 @@ for (const kernelConfig of kernelConfigs) {
   registerKernel(kernelConfig);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/index.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -54892,7 +54892,7 @@ for (const kernelConfig of kernelConfigs) {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -54972,7 +54972,7 @@ function getWebGLRenderingContext(webGLVersion) {
   return canvas.getContext("webgl2", WEBGL_ATTRIBUTES);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -55078,7 +55078,7 @@ function getTextureConfig(gl, textureHalfFloatExtension) {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -55527,7 +55527,7 @@ function assertNotComplex2(tensor2, opName) {
   });
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -55604,7 +55604,7 @@ ENV3.registerFlag("WEBGL_FLUSH_THRESHOLD", () => {
   }
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -55714,7 +55714,7 @@ function getGlslDifferences() {
   };
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -55788,7 +55788,7 @@ var ENCODE_FLOAT_SNIPPET = `
   }
 `;
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -55839,7 +55839,7 @@ var DecodeMatrixProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -55890,7 +55890,7 @@ var DecodeMatrixPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -55924,7 +55924,7 @@ var EncodeFloatProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -55961,7 +55961,7 @@ var EncodeFloatPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -56022,7 +56022,7 @@ var EncodeMatrixProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -56106,7 +56106,7 @@ var EncodeMatrixPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -56266,7 +56266,7 @@ function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
   return packedRGBA;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -56676,7 +56676,7 @@ function linearSearchLastTrue(arr) {
   return i - 1;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -57843,7 +57843,7 @@ function getSqueezedParams(params, keptDims) {
   return keptDims.map((d) => params[d]).join(", ");
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -57990,7 +57990,7 @@ function makeShaderKey(program, inputs, output) {
   return key;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -58009,7 +58009,7 @@ function makeShaderKey(program, inputs, output) {
  */
 var {addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, lessImpl: lessImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, stridedSliceImpl: stridedSliceImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU} = shared_exports;
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -58049,7 +58049,7 @@ function getSourceCoords(rank, dims) {
   return coords2;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -58156,7 +58156,7 @@ function getOutput(shape, dims) {
           rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -58231,7 +58231,7 @@ function getReshapedInputCoords(shape) {
   `;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -58438,7 +58438,7 @@ function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
   return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -58485,7 +58485,7 @@ var RELU6 = CHECK_NAN_SNIPPET + `
 `;
 var CLONE = "return x;";
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -58556,7 +58556,7 @@ var UnaryOpPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -58596,7 +58596,7 @@ var UnpackProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -59315,11 +59315,11 @@ function float32ToTypedArray(a, dtype) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js
 /** @license See the LICENSE file. */
 var version6 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -59340,7 +59340,7 @@ if (device_util_exports.isBrowser()) {
   registerBackend("webgl", () => new MathBackendWebGL(), 2);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -59379,7 +59379,7 @@ var BinaryOpProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -59461,7 +59461,7 @@ var BinaryOpPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59490,7 +59490,7 @@ var identityConfig2 = {
   kernelFunc: identity3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59523,7 +59523,7 @@ var complexConfig2 = {
   kernelFunc: complex3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59561,7 +59561,7 @@ var leakyReluConfig2 = {
   kernelFunc: leakyRelu3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59595,7 +59595,7 @@ var preluConfig2 = {
   kernelFunc: prelu4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59728,7 +59728,7 @@ function mapActivationToShaderProgram(activation2, packed = false) {
   throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -59828,7 +59828,7 @@ var MatMulPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -59870,7 +59870,7 @@ var BinaryOpComplexProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59949,7 +59949,7 @@ var multiplyConfig2 = {
   kernelFunc: multiply3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -59986,7 +59986,7 @@ function packedReshape(input2, afterShape, backend2) {
   return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60025,7 +60025,7 @@ var reshapeConfig2 = {
   kernelFunc: reshape4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60115,7 +60115,7 @@ var MeanProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -60264,7 +60264,7 @@ var ReduceProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60315,7 +60315,7 @@ function reduce(x, dtype, reductionType, backend2) {
   return result;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -60364,7 +60364,7 @@ function getSwitchedCoords(newDim) {
   return switchedCoords.join();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -60425,7 +60425,7 @@ var TransposePackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60447,7 +60447,7 @@ function transposeImpl2(x, perm, backend2) {
   return backend2.runWebGLProgram(program, [x], x.dtype);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60497,7 +60497,7 @@ function sumImpl(x, axis, keepDims, backend2) {
   return out;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60526,7 +60526,7 @@ var sumConfig2 = {
   kernelFunc: sum4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60572,7 +60572,7 @@ var transposeConfig2 = {
   kernelFunc: transpose3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60679,7 +60679,7 @@ function batchMatMulImpl({a, b, transposeA, transposeB, backend: backend2, bias
   return outReshaped;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60718,7 +60718,7 @@ var _fusedMatMulConfig2 = {
   kernelFunc: _fusedMatMul2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60758,7 +60758,7 @@ var absConfig2 = {
   kernelFunc: abs3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60788,7 +60788,7 @@ var acosConfig2 = {
   kernelFunc: acos3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60815,7 +60815,7 @@ var acoshConfig2 = {
   kernelFunc: acosh3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60845,7 +60845,7 @@ var addConfig2 = {
   kernelFunc: addKernelFunc
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -60885,7 +60885,7 @@ var AddNProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -60927,7 +60927,7 @@ var AddNPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -60968,7 +60968,7 @@ var addNConfig2 = {
   kernelFunc: addN3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61023,7 +61023,7 @@ var allConfig2 = {
   kernelFunc: all3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61078,7 +61078,7 @@ var anyConfig2 = {
   kernelFunc: any3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -61129,7 +61129,7 @@ var ArgMinMaxProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -61252,7 +61252,7 @@ var ArgMinMaxPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61323,7 +61323,7 @@ function argMinMaxReduce(backend2, x, axis, reduceType) {
   return argReducePacked(backend2, x, reduceType);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61364,7 +61364,7 @@ var argMaxConfig2 = {
   kernelFunc: argMax3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61405,7 +61405,7 @@ var argMinConfig2 = {
   kernelFunc: argMin3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61435,7 +61435,7 @@ var asinConfig2 = {
   kernelFunc: asin3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61460,7 +61460,7 @@ var asinhConfig2 = {
   kernelFunc: asinh3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61487,7 +61487,7 @@ var atanConfig2 = {
   kernelFunc: atan4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61520,7 +61520,7 @@ var atan2Config2 = {
   kernelFunc: atan23
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61547,7 +61547,7 @@ var atanhConfig2 = {
   kernelFunc: atanh3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -61949,7 +61949,7 @@ var Pool3DProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -61986,7 +61986,7 @@ var avgPoolConfig2 = {
   kernelFunc: avgPool3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62018,7 +62018,7 @@ var avgPool3DConfig2 = {
   kernelFunc: avgPool3D2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -62175,7 +62175,7 @@ var AvgPool3DBackpropProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62208,7 +62208,7 @@ var avgPoolGrad3DConfig = {
   kernelFunc: avgPool3DGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62241,7 +62241,7 @@ var avgPoolGradConfig3 = {
   kernelFunc: avgPoolGrad3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62270,7 +62270,7 @@ var batchMatMulConfig2 = {
   kernelFunc: batchMatMul2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -62320,7 +62320,7 @@ var BatchNormProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -62374,7 +62374,7 @@ var BatchNormPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62421,7 +62421,7 @@ var batchNormConfig2 = {
   kernelFunc: batchNorm3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -62489,7 +62489,7 @@ function getCoords(rank) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -62568,7 +62568,7 @@ var SlicePackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62635,7 +62635,7 @@ var sliceConfig2 = {
   kernelFunc: slice3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62688,7 +62688,7 @@ var batchToSpaceNDConfig2 = {
   kernelFunc: batchToSpaceND3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62720,7 +62720,7 @@ var bincountConfig2 = {
   kernelFunc: bincount3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62745,7 +62745,7 @@ var notEqualConfig2 = {
   kernelFunc: notEqual3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62774,7 +62774,7 @@ var realConfig2 = {
   kernelFunc: real3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62798,7 +62798,7 @@ function int(input2, backend2) {
   return {dataId: output.dataId, shape: output.shape, dtype: output.dtype};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62858,7 +62858,7 @@ var castConfig2 = {
   kernelFunc: cast4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -62883,7 +62883,7 @@ var ceilConfig2 = {
   kernelFunc: ceil3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -62931,7 +62931,7 @@ var ClipProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -62982,7 +62982,7 @@ var ClipPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63018,7 +63018,7 @@ var clipByValueConfig = {
   kernelFunc: clipByValue2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -63056,7 +63056,7 @@ var ComplexAbsProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63097,7 +63097,7 @@ var complexAbsConfig2 = {
   kernelFunc: complexAbs2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -63144,7 +63144,7 @@ var ConcatProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -63241,7 +63241,7 @@ function shiftedChannels(channels, channel, shift) {
   return res.join();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63270,7 +63270,7 @@ var imagConfig2 = {
   kernelFunc: imag3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63344,7 +63344,7 @@ function computeTensors2D(inputs, axis, backend2) {
   return {tensors2D, outShape};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63383,7 +63383,7 @@ var concatConfig2 = {
   kernelFunc: concat3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -63686,7 +63686,7 @@ var Conv3DProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -63770,7 +63770,7 @@ var Im2ColPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63921,7 +63921,7 @@ function conv2dWithIm2Row({x, filter, convInfo, backend: backend2, bias = null,
   return out;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -63963,7 +63963,7 @@ var conv2DConfig2 = {
   kernelFunc: conv2d4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -64233,7 +64233,7 @@ var Conv3DDerInputProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64265,7 +64265,7 @@ var conv2DBackpropFilterConfig2 = {
   kernelFunc: conv2DBackpropFilter3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64297,7 +64297,7 @@ var conv2DBackpropInputConfig2 = {
   kernelFunc: conv2DBackpropInput3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64328,7 +64328,7 @@ var conv3DConfig2 = {
   kernelFunc: conv3D2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64359,7 +64359,7 @@ var conv3DBackpropFilterV2Config2 = {
   kernelFunc: conv3DBackpropFilterV22
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64390,7 +64390,7 @@ var conv3DBackpropInputConfig = {
   kernelFunc: conv3DBackpropInput2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64417,7 +64417,7 @@ var cosConfig2 = {
   kernelFunc: cos3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64445,7 +64445,7 @@ var coshConfig2 = {
   kernelFunc: cosh3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -64555,7 +64555,7 @@ var CropAndResizeProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64585,7 +64585,7 @@ var cropAndResizeConfig2 = {
   kernelFunc: cropAndResize3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/cumsum_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/cumsum_gpu.js
 var CumSumProgram = class {
   constructor(shape, exclusive, reverse5) {
     this.variableNames = ["x"];
@@ -64654,7 +64654,7 @@ function getFinalCoord(rank, name) {
   }
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64715,7 +64715,7 @@ var cumsumConfig2 = {
   kernelFunc: cumsum3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64755,7 +64755,7 @@ var denseBincountConfig2 = {
   kernelFunc: denseBincount3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -64837,7 +64837,7 @@ var DepthToSpaceProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -64876,7 +64876,7 @@ var depthToSpaceConfig2 = {
   kernelFunc: depthToSpace3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -64989,7 +64989,7 @@ var DepthwiseConv2DProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -65261,7 +65261,7 @@ var DepthwiseConvPacked2DProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65302,7 +65302,7 @@ var depthwiseConv2dNativeConfig2 = {
   kernelFunc: depthwiseConv2dNative2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -65426,7 +65426,7 @@ var DepthwiseConv2DDerInputProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65457,7 +65457,7 @@ var depthwiseConv2dNativeBackpropFilterConfig2 = {
   kernelFunc: depthwiseConv2dNativeBackpropFilter3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65488,7 +65488,7 @@ var depthwiseConv2dNativeBackpropInputConfig2 = {
   kernelFunc: depthwiseConv2dNativeBackpropInput3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -65519,7 +65519,7 @@ var DiagProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65555,7 +65555,7 @@ var diagConfig2 = {
   kernelFunc: diag3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -65620,7 +65620,7 @@ var Dilation2DProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65655,7 +65655,7 @@ var dilation2DConfig = {
   kernelFunc: dilation2D
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65690,7 +65690,7 @@ var eluConfig2 = {
   kernelFunc: elu5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65724,7 +65724,7 @@ var eluGradConfig3 = {
   kernelFunc: eluGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65752,7 +65752,7 @@ var equalConfig2 = {
   kernelFunc: equal3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65792,7 +65792,7 @@ var erfConfig2 = {
   kernelFunc: erf3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65817,7 +65817,7 @@ var expConfig2 = {
   kernelFunc: exp3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65854,7 +65854,7 @@ var expandDimsConfig2 = {
   kernelFunc: expandDims4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65879,7 +65879,7 @@ var expm1Config2 = {
   kernelFunc: expm13
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -65948,7 +65948,7 @@ var FFTProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -65997,7 +65997,7 @@ function fftImpl2(x, inverse, backend2) {
   return complexOutputReshaped;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66025,7 +66025,7 @@ var fftConfig2 = {
   kernelFunc: fft3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -66065,7 +66065,7 @@ var FillProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66103,7 +66103,7 @@ var fillConfig2 = {
   kernelFunc: fill3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66144,7 +66144,7 @@ var FlipLeftRightProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66173,7 +66173,7 @@ var flipLeftRightConfig2 = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66198,7 +66198,7 @@ var floorConfig2 = {
   kernelFunc: floor3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66255,7 +66255,7 @@ var floorDivConfig2 = {
   kernelFunc: floorDiv3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -66304,7 +66304,7 @@ var FromPixelsProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -66367,7 +66367,7 @@ var FromPixelsPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -66420,7 +66420,7 @@ function fromPixels2(args) {
   return res;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66498,7 +66498,7 @@ var fusedConv2DConfig2 = {
   kernelFunc: fusedConv2d
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66559,7 +66559,7 @@ var fusedDepthwiseConv2DConfig2 = {
   kernelFunc: fusedDepthwiseConv2D2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js
 var GatherNDProgram = class {
   constructor(sliceDim, strides, shape) {
     this.sliceDim = sliceDim;
@@ -66584,7 +66584,7 @@ var GatherNDProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66627,7 +66627,7 @@ var gatherNdConfig2 = {
   kernelFunc: gatherNd2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -66672,7 +66672,7 @@ function getSourceCoords2(aShape, axis) {
   return sourceCoords.join();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66742,7 +66742,7 @@ var gatherV2Config2 = {
   kernelFunc: gatherV22
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66775,7 +66775,7 @@ var greaterConfig2 = {
   kernelFunc: greater4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66807,7 +66807,7 @@ var greaterEqualConfig2 = {
   kernelFunc: greaterEqual3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66835,7 +66835,7 @@ var ifftConfig2 = {
   kernelFunc: ifft3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66860,7 +66860,7 @@ var isFiniteConfig2 = {
   kernelFunc: isFinite4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66885,7 +66885,7 @@ var isInfConfig2 = {
   kernelFunc: isInf3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66910,7 +66910,7 @@ var isNaNConfig2 = {
   kernelFunc: isNaN4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66943,7 +66943,7 @@ var lessConfig2 = {
   kernelFunc: less4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -66971,7 +66971,7 @@ var lessEqualConfig2 = {
   kernelFunc: lessEqual3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67000,7 +67000,7 @@ var linSpaceConfig2 = {
   kernelFunc: linSpace2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67036,7 +67036,7 @@ var logConfig2 = {
   kernelFunc: log4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67061,7 +67061,7 @@ var log1pConfig2 = {
   kernelFunc: log1p3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67095,7 +67095,7 @@ var logicalAndConfig2 = {
   kernelFunc: logicalAnd3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67120,7 +67120,7 @@ var logicalNotConfig2 = {
   kernelFunc: logicalNot3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67151,7 +67151,7 @@ var logicalOrConfig2 = {
   kernelFunc: logicalOr3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -67207,7 +67207,7 @@ var LRNProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -67308,7 +67308,7 @@ var LRNPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67338,7 +67338,7 @@ var LRNConfig = {
   kernelFunc: lrn
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -67424,7 +67424,7 @@ var LRNGradProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67454,7 +67454,7 @@ var LRNGradConfig = {
   kernelFunc: lrnGrad
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67483,7 +67483,7 @@ function maxImpl2(x, reduceShape, outShape, backend2) {
   return reshapedOutput;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67556,7 +67556,7 @@ var maxConfig2 = {
   kernelFunc: max4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67593,7 +67593,7 @@ var maximumConfig2 = {
   kernelFunc: maximum4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67630,7 +67630,7 @@ var maxPoolConfig2 = {
   kernelFunc: maxPool3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67662,7 +67662,7 @@ var maxPool3DConfig2 = {
   kernelFunc: maxPool3d2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -67825,7 +67825,7 @@ var MaxPool3DBackpropProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67862,7 +67862,7 @@ var maxPoolGrad3DConfig = {
   kernelFunc: maxPool3DGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67900,7 +67900,7 @@ var maxPoolGradConfig3 = {
   kernelFunc: maxPoolGrad3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67925,7 +67925,7 @@ function maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {
   return [poolOutput, indexOutput];
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67958,7 +67958,7 @@ var maxPoolWithArgmaxConfig2 = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -67987,7 +67987,7 @@ function meanImpl(x, reduceShape, outShape, backend2) {
   return reshapedOutput;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68051,7 +68051,7 @@ var meanConfig2 = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68106,7 +68106,7 @@ var minConfig2 = {
   kernelFunc: min4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68143,7 +68143,7 @@ var minimumConfig2 = {
   kernelFunc: minimum4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68207,7 +68207,7 @@ var MirrorPadProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68307,7 +68307,7 @@ var MirrorPadPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68337,7 +68337,7 @@ var mirrorPadConfig2 = {
   kernelFunc: mirrorPadKernelFunc
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68372,7 +68372,7 @@ var modConfig2 = {
   kernelFunc: mod3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -68427,7 +68427,7 @@ var MultinomialProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68475,7 +68475,7 @@ var realDivConfig2 = {
   kernelFunc: realDiv
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68505,7 +68505,7 @@ var subConfig2 = {
   kernelFunc: sub3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68553,7 +68553,7 @@ var softmaxConfig2 = {
   kernelFunc: softmax4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68591,7 +68591,7 @@ var multinomialConfig2 = {
   kernelFunc: multinomial3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68631,7 +68631,7 @@ var negConfig2 = {
   kernelFunc: neg3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68665,7 +68665,7 @@ var nonMaxSuppressionV3Config2 = {
   kernelFunc: nonMaxSuppressionV32
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68702,7 +68702,7 @@ var nonMaxSuppressionV4Config2 = {
   kernelFunc: nonMaxSuppressionV42
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68743,7 +68743,7 @@ var nonMaxSuppressionV5Config2 = {
   kernelFunc: nonMaxSuppressionV52
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -68775,7 +68775,7 @@ var OneHotProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68812,7 +68812,7 @@ var oneHotConfig2 = {
   kernelFunc: oneHot3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68860,7 +68860,7 @@ var zerosLikeConfig2 = {
   kernelFunc: zerosLike3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68903,7 +68903,7 @@ var onesLikeConfig2 = {
   kernelFunc: onesLike3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -68948,7 +68948,7 @@ var packConfig2 = {
   kernelFunc: pack2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -69017,7 +69017,7 @@ var PadProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -69097,7 +69097,7 @@ var PadPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69128,7 +69128,7 @@ var padV2Config2 = {
   kernelFunc: padV22
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69179,7 +69179,7 @@ var powConfig2 = {
   kernelFunc: pow3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69241,7 +69241,7 @@ var prodConfig2 = {
   kernelFunc: prod3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69270,7 +69270,7 @@ var rangeConfig2 = {
   kernelFunc: range4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69295,7 +69295,7 @@ var reciprocalConfig2 = {
   kernelFunc: reciprocal3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69333,7 +69333,7 @@ var reluConfig2 = {
   kernelFunc: relu3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69371,7 +69371,7 @@ var relu6Config2 = {
   kernelFunc: relu63
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -69445,7 +69445,7 @@ var ResizeBilinearProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -69565,7 +69565,7 @@ var ResizeBilinearPackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69596,7 +69596,7 @@ var resizeBilinearConfig2 = {
   kernelFunc: resizeBilinear3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -69719,7 +69719,7 @@ var ResizeBilinearBackpropProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69749,7 +69749,7 @@ var resizeBilinearGradConfig3 = {
   kernelFunc: resizeBilinearGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -69813,7 +69813,7 @@ var ResizeNearestNeighborProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69844,7 +69844,7 @@ var resizeNearestNeighborConfig2 = {
   kernelFunc: resizeNearestNeighbor3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -69956,7 +69956,7 @@ var ResizeNearestNeigborBackpropProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -69986,7 +69986,7 @@ var resizeNearestNeighborGradConfig3 = {
   kernelFunc: resizeNearestNeighborGrad2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -70037,7 +70037,7 @@ var ReverseProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -70133,7 +70133,7 @@ var ReversePackedProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70168,7 +70168,7 @@ var reverseConfig2 = {
   kernelFunc: reverse3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70230,7 +70230,7 @@ var RotateProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70262,7 +70262,7 @@ var rotateWithOffsetConfig2 = {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70302,7 +70302,7 @@ var roundConfig2 = {
   kernelFunc: round4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70327,7 +70327,7 @@ var rsqrtConfig2 = {
   kernelFunc: rsqrt3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -70389,7 +70389,7 @@ var ScatterProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70433,7 +70433,7 @@ var scatterNdConfig2 = {
   kernelFunc: scatterNd2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -70490,7 +70490,7 @@ var SelectProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70519,7 +70519,7 @@ var selectConfig2 = {
   kernelFunc: select2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70550,7 +70550,7 @@ var seluConfig2 = {
   kernelFunc: selu3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70575,7 +70575,7 @@ var sigmoidConfig2 = {
   kernelFunc: sigmoid3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70603,7 +70603,7 @@ var signConfig2 = {
   kernelFunc: sign3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70630,7 +70630,7 @@ var sinConfig2 = {
   kernelFunc: sin3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70658,7 +70658,7 @@ var sinhConfig2 = {
   kernelFunc: sinh3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70703,7 +70703,7 @@ var softplusConfig2 = {
   kernelFunc: softplus3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70759,7 +70759,7 @@ var spaceToBatchNDConfig2 = {
   kernelFunc: spaceToBatchND3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70794,7 +70794,7 @@ var sparseToDenseConfig2 = {
   kernelFunc: sparseToDense3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70834,7 +70834,7 @@ var splitVConfig2 = {
   kernelFunc: splitV2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70859,7 +70859,7 @@ var sqrtConfig2 = {
   kernelFunc: sqrt3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -70884,7 +70884,7 @@ var squareConfig2 = {
   kernelFunc: square3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70909,7 +70909,7 @@ var squaredDifferenceConfig2 = {
   kernelFunc: squaredDifference3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -70940,7 +70940,7 @@ var stepConfig2 = {
   kernelFunc: step3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -70986,7 +70986,7 @@ var StridedSliceProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71040,7 +71040,7 @@ var stridedSliceConfig2 = {
   kernelFunc: stridedSlice3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71065,7 +71065,7 @@ var tanConfig2 = {
   kernelFunc: tan3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71093,7 +71093,7 @@ var tanhConfig2 = {
   kernelFunc: tanh4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -71145,7 +71145,7 @@ function getSourceCoords3(aShape) {
   return sourceCoords.join();
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71183,7 +71183,7 @@ var tileConfig2 = {
   kernelFunc: tile4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71217,7 +71217,7 @@ var topKConfig2 = {
   kernelFunc: topK2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -71373,7 +71373,7 @@ var TransformProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -71411,7 +71411,7 @@ var transformConfig2 = {
   kernelFunc: transform3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71447,7 +71447,7 @@ var uniqueConfig2 = {
   kernelFunc: unique4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71502,7 +71502,7 @@ var unpackConfig2 = {
   kernelFunc: unpack2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -71651,7 +71651,7 @@ var SegmentOpProgram = class {
   }
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71729,7 +71729,7 @@ var unsortedSegmentSumConfig2 = {
   kernelFunc: unsortedSegmentSum3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71905,7 +71905,7 @@ for (const kernelConfig of kernelConfigs2) {
   registerKernel(kernelConfig);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-webgl@3.3.0_@tensorflow+tfjs-core@3.3.0/node_modules/@tensorflow/tfjs-backend-webgl/dist/index.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -71923,11 +71923,11 @@ for (const kernelConfig of kernelConfigs2) {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs@3.3.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs@3.3.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs/dist/version.js
 /** @license See the LICENSE file. */
 var version7 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs@3.3.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs@3.3.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs/dist/index.js
 /**
  * @license
  * Copyright 2018 Google LLC. All Rights Reserved.
@@ -71954,7 +71954,7 @@ var version8 = {
   tfjs: version7
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -71988,7 +71988,7 @@ var FusableActivation;
   FusableActivation2[FusableActivation2["leakyrelu"] = 4] = "leakyrelu";
 })(FusableActivation || (FusableActivation = {}));
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72062,7 +72062,7 @@ var fusedMatMulConfig = {
   kernelFunc: fusedBatchMatMul
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72098,7 +72098,7 @@ function createUnaryKernelConfig(kernelName) {
   return {kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72117,7 +72117,7 @@ function createUnaryKernelConfig(kernelName) {
  */
 var absConfig3 = createUnaryKernelConfig(Abs);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72181,7 +72181,7 @@ function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {
   return {kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72201,7 +72201,7 @@ function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {
 var supportsFullBroadcast = true;
 var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72246,7 +72246,7 @@ var addNConfig3 = {
   kernelFunc: addn
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -72277,7 +72277,7 @@ var identityConfig3 = {
   kernelFunc: identity4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72370,7 +72370,7 @@ var transposeConfig3 = {
   setupFunc: setup2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -72411,7 +72411,7 @@ function permuteAxesAndTranspose(x, axis, backend2) {
   return {transposed: xTransposed, originalAxes, axes, inputWasTransposed};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72471,7 +72471,7 @@ var argMaxConfig3 = {
   setupFunc: setup3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72540,7 +72540,7 @@ var avgPoolConfig3 = {
   kernelFunc: avgPool4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72573,7 +72573,7 @@ var reshapeConfig3 = {
   kernelFunc: reshape5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72652,7 +72652,7 @@ var batchMatMulConfig3 = {
   kernelFunc: batchMatMul3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72683,7 +72683,7 @@ var castConfig3 = {
   kernelFunc: cast5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -72702,7 +72702,7 @@ var castConfig3 = {
  */
 var ceilConfig3 = createUnaryKernelConfig(Ceil);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72745,7 +72745,7 @@ var clipByValueConfig2 = {
   kernelFunc: clip2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -72788,7 +72788,7 @@ function concatImpl3(inputs, outShape, dtype, simplyConcat) {
   return outVals;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -72824,7 +72824,7 @@ function rangeImpl2(start, stop, step5, dtype) {
   return values;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -72866,7 +72866,7 @@ function sliceImpl2(vals, begin, size, shape, dtype) {
   return outBuf.values;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-cpu@3.3.0/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -72884,7 +72884,7 @@ function sliceImpl2(vals, begin, size, shape, dtype) {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernel_utils/shared.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernel_utils/shared.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -72902,7 +72902,7 @@ function sliceImpl2(vals, begin, size, shape, dtype) {
  * =============================================================================
  */
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -72979,7 +72979,7 @@ var concatConfig3 = {
   kernelFunc: concat4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73056,7 +73056,7 @@ var conv2DConfig3 = {
   kernelFunc: conv2d5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -73141,7 +73141,7 @@ var conv2DBackpropInputConfig3 = {
   kernelFunc: conv2DBackpropInput4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73160,7 +73160,7 @@ var conv2DBackpropInputConfig3 = {
  */
 var cosConfig3 = createUnaryKernelConfig(Cos);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73229,7 +73229,7 @@ var cropAndResizeConfig3 = {
   kernelFunc: cropAndResize4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -73291,7 +73291,7 @@ var cumsumConfig3 = {
   kernelFunc: cumsum4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73353,7 +73353,7 @@ var depthToSpaceConfig3 = {
   kernelFunc: depthToSpace4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73430,7 +73430,7 @@ var depthwiseConv2dNativeConfig3 = {
   kernelFunc: depthwiseConv2d5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -73450,7 +73450,7 @@ var depthwiseConv2dNativeConfig3 = {
 var supportsFullBroadcast2 = false;
 var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73469,7 +73469,7 @@ var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool
  */
 var expConfig3 = createUnaryKernelConfig(Exp);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73506,7 +73506,7 @@ var expandDimsConfig3 = {
   kernelFunc: expandDims5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -73536,7 +73536,7 @@ var fillConfig3 = {
   kernelFunc: fill4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73581,7 +73581,7 @@ var flipLeftRightConfig3 = {
   setupFunc: setup13
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -73600,7 +73600,7 @@ var flipLeftRightConfig3 = {
  */
 var floorConfig3 = createUnaryKernelConfig(Floor);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73620,7 +73620,7 @@ var floorConfig3 = createUnaryKernelConfig(Floor);
 var supportsFullBroadcast3 = false;
 var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73665,7 +73665,7 @@ var fusedBatchNormConfig = {
   kernelFunc: fusedBatchNorm
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73764,7 +73764,7 @@ var fusedConv2DConfig3 = {
   kernelFunc: fusedConv2d2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73863,7 +73863,7 @@ var fusedDepthwiseConv2DConfig3 = {
   kernelFunc: fusedDepthwiseConv2d
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -73919,7 +73919,7 @@ var gatherNdConfig3 = {
   kernelFunc: gatherNd3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74004,7 +74004,7 @@ var gatherV2Config3 = {
   kernelFunc: gatherV23
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74024,7 +74024,7 @@ var gatherV2Config3 = {
 var supportsFullBroadcast4 = false;
 var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74044,7 +74044,7 @@ var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "
 var supportsFullBroadcast5 = false;
 var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74086,7 +74086,7 @@ var leakyReluConfig3 = {
   kernelFunc: leakyRelu4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74106,7 +74106,7 @@ var leakyReluConfig3 = {
 var supportsFullBroadcast6 = false;
 var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74126,7 +74126,7 @@ var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool")
 var supportsFullBroadcast7 = false;
 var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74145,7 +74145,7 @@ var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast
  */
 var logConfig3 = createUnaryKernelConfig(Log);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74165,7 +74165,7 @@ var logConfig3 = createUnaryKernelConfig(Log);
 var supportsFullBroadcast8 = false;
 var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74224,7 +74224,7 @@ var maxConfig3 = {
   kernelFunc: max5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74244,7 +74244,7 @@ var maxConfig3 = {
 var supportsFullBroadcast9 = false;
 var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74316,7 +74316,7 @@ var maxPoolConfig3 = {
   kernelFunc: maxPool4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74386,7 +74386,7 @@ var meanConfig3 = {
   kernelFunc: mean3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74447,7 +74447,7 @@ var minConfig3 = {
   kernelFunc: min5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74467,7 +74467,7 @@ var minConfig3 = {
 var supportsFullBroadcast10 = false;
 var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74487,7 +74487,7 @@ var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10);
 var supportsFullBroadcast11 = true;
 var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74506,7 +74506,7 @@ var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11
  */
 var negConfig3 = createUnaryKernelConfig(Neg);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74533,7 +74533,7 @@ function parseResultStruct(backend2, resOffset) {
   return {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs};
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74580,7 +74580,7 @@ var nonMaxSuppressionV3Config3 = {
   kernelFunc
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74628,7 +74628,7 @@ var nonMaxSuppressionV4Config3 = {
   kernelFunc: nonMaxSuppressionV43
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74676,7 +74676,7 @@ var nonMaxSuppressionV5Config3 = {
   kernelFunc: kernelFunc2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74696,7 +74696,7 @@ var nonMaxSuppressionV5Config3 = {
 var supportsFullBroadcast12 = false;
 var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, "bool");
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74741,7 +74741,7 @@ var oneHotConfig3 = {
   kernelFunc: oneHot4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74771,7 +74771,7 @@ var onesLikeConfig3 = {
   kernelFunc: onesLike4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74816,7 +74816,7 @@ var packConfig3 = {
   kernelFunc: pack3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74867,7 +74867,7 @@ var padV2Config3 = {
   setupFunc: setup27
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74887,7 +74887,7 @@ var padV2Config3 = {
 var supportsFullBroadcast13 = false;
 var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast13);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -74929,7 +74929,7 @@ var preluConfig3 = {
   kernelFunc: prelu5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -74996,7 +74996,7 @@ var prodConfig3 = {
   kernelFunc: prod4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75028,7 +75028,7 @@ var rangeConfig3 = {
   kernelFunc: range5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75048,7 +75048,7 @@ var rangeConfig3 = {
 var supportsFullBroadcast14 = true;
 var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75067,7 +75067,7 @@ var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14);
  */
 var reluConfig3 = createUnaryKernelConfig(Relu);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75086,7 +75086,7 @@ var reluConfig3 = createUnaryKernelConfig(Relu);
  */
 var relu6Config3 = createUnaryKernelConfig(Relu6);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75150,7 +75150,7 @@ var resizeBilinearConfig3 = {
   kernelFunc: resizeBilinear4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75203,7 +75203,7 @@ var reverseConfig3 = {
   setupFunc: setup31
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75259,7 +75259,7 @@ var rotateWithOffsetConfig3 = {
   setupFunc: setup32
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js
 /**
  * @license
  * Copyright 2021 Google LLC. All Rights Reserved.
@@ -75278,7 +75278,7 @@ var rotateWithOffsetConfig3 = {
  */
 var roundConfig3 = createUnaryKernelConfig(Round);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75297,7 +75297,7 @@ var roundConfig3 = createUnaryKernelConfig(Round);
  */
 var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75353,7 +75353,7 @@ var scatterNdConfig3 = {
   kernelFunc: scatterNd3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75401,7 +75401,7 @@ var selectConfig3 = {
   setupFunc: setup34
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75440,7 +75440,7 @@ var sigmoidConfig3 = {
   kernelFunc: sigmoid4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75459,7 +75459,7 @@ var sigmoidConfig3 = {
  */
 var sinConfig3 = createUnaryKernelConfig(Sin);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75564,7 +75564,7 @@ var sliceConfig3 = {
   kernelFunc: slice4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75610,7 +75610,7 @@ var softmaxConfig3 = {
   kernelFunc: softmax5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75649,7 +75649,7 @@ var splitVConfig3 = {
   kernelFunc: splitV3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75668,7 +75668,7 @@ var splitVConfig3 = {
  */
 var sqrtConfig3 = createUnaryKernelConfig(Sqrt);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75687,7 +75687,7 @@ var sqrtConfig3 = createUnaryKernelConfig(Sqrt);
  */
 var squareConfig3 = createUnaryKernelConfig(Square);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75707,7 +75707,7 @@ var squareConfig3 = createUnaryKernelConfig(Square);
 var supportsFullBroadcast15 = true;
 var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75749,7 +75749,7 @@ var stepConfig3 = {
   kernelFunc: step4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -75851,7 +75851,7 @@ var stridedSliceConfig3 = {
   kernelFunc: stridedSlice4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75871,7 +75871,7 @@ var stridedSliceConfig3 = {
 var supportsFullBroadcast16 = true;
 var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast16);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75933,7 +75933,7 @@ var sumConfig3 = {
   kernelFunc: sum5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -75952,7 +75952,7 @@ var sumConfig3 = {
  */
 var tanhConfig3 = createUnaryKernelConfig(Tanh);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -76003,7 +76003,7 @@ var tileConfig3 = {
   kernelFunc: tile5
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -76054,7 +76054,7 @@ var topKConfig3 = {
   kernelFunc: topk2
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
@@ -76103,7 +76103,7 @@ var unpackConfig3 = {
   kernelFunc: unpack3
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -76133,7 +76133,7 @@ var zerosLikeConfig3 = {
   kernelFunc: zerosLike4
 };
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -76243,7 +76243,7 @@ for (const kernelConfig of kernelConfigs3) {
   registerKernel(kernelConfig);
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -76342,13 +76342,13 @@ ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
   }
 });
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
 var import_tfjs_backend_wasm_threaded_simd = __toModule(require_tfjs_backend_wasm_threaded_simd());
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js
 var wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");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;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(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;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}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);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}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);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}';
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
 var import_tfjs_backend_wasm = __toModule(require_tfjs_backend_wasm());
 /**
  * @license
@@ -76619,11 +76619,11 @@ function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {
   customFetch = usePlatformFetch;
 }
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js
 /** @license See the LICENSE file. */
 var version9 = "3.3.0";
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js
 /**
  * @license
  * Copyright 2020 Google LLC. All Rights Reserved.
@@ -76646,7 +76646,7 @@ registerBackend("wasm", async () => {
   return new BackendWasm(wasm);
 }, WASM_PRIORITY);
 
-// node_modules/.pnpm/@tensorflow/tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/index.js
+// node_modules/.pnpm/@tensorflow#tfjs-backend-wasm@3.3.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/index.js
 /**
  * @license
  * Copyright 2019 Google LLC. All Rights Reserved.
diff --git a/dist/tfjs.esm.js.map b/dist/tfjs.esm.js.map
index c4f02a7..97093b3 100644
--- a/dist/tfjs.esm.js.map
+++ b/dist/tfjs.esm.js.map
@@ -1,6 +1,6 @@
 {
   "version": 3,
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   "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename 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xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"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\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var 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print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Step=Module[\"_Step\"]=function(){return(_Step=Module[\"_Step\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Ba\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Ca\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");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;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(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;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}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);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command 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   "names": []
diff --git a/package.json b/package.json
index 6134dea..bf47484 100644
--- a/package.json
+++ b/package.json
@@ -46,8 +46,8 @@
     "@tensorflow/tfjs-node": "^3.3.0",
     "@tensorflow/tfjs-node-gpu": "^3.3.0",
     "@types/node": "^14.14.37",
-    "@typescript-eslint/eslint-plugin": "^4.20.0",
-    "@typescript-eslint/parser": "^4.20.0",
+    "@typescript-eslint/eslint-plugin": "^4.21.0",
+    "@typescript-eslint/parser": "^4.21.0",
     "@vladmandic/pilogger": "^0.2.15",
     "canvas": "^2.7.0",
     "chokidar": "^3.5.1",
@@ -62,7 +62,7 @@
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
     "simple-git": "^2.37.0",
-    "tslib": "^2.1.0",
+    "tslib": "^2.2.0",
     "typedoc": "^0.20.35",
     "typescript": "^4.2.3"
   }