TensorFlow Source

2021-05-25 09:17:10 -05:00
1 parent 3d3ab2548d
commit 8c7ff0c1d9
@@ -0,0 +1 @@
+{"version":3,"file":"sparse.js","sourceRoot":"","sources":["../../../src/operations/op_list/sparse.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,CAAC,MAAM,IAAI,GAAe,CAAC;QAC/B,UAAU,EAAE,eAAe;QAC3B,UAAU,EAAE,QAAQ;QACpB,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,cAAc,EAAE,MAAM,EAAE,QAAQ,EAAC;YACtD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,QAAQ,EAAC;YACpD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,UAAU,EAAE,MAAM,EAAE,QAAQ,EAAC;SACnD;QACD,OAAO,EACH,CAAC,EAAC,QAAQ,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,OAAO,EAAE,cAAc,EAAE,IAAI,EAAC,CAAC;KAC9E,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [{\n  'tfOpName': 'SparseReshape',\n  'category': 'sparse',\n  'inputs': [\n    {'start': 0, 'name': 'inputIndices', 'type': 'tensor'},\n    {'start': 1, 'name': 'inputShape', 'type': 'tensor'},\n    {'start': 2, 'name': 'newShape', 'type': 'tensor'},\n  ],\n  'attrs':\n      [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}]\n}];\n"]}
@@ -1 +1 @@
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+{"version":3,"file":"spectral.js","sourceRoot":"","sources":["../../../src/operations/op_list/spectral.ts"],"names":[],"mappings":"AAEA;;;;;;;;;;;;;;;GAeG;AAEH,MAAM,CAAC,MAAM,IAAI,GAAe;IAC9B;QACE,UAAU,EAAE,KAAK;QACjB,UAAU,EAAE,UAAU;QACtB,QAAQ,EAAE,CAAC,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC,CAAC;KACxD;IACD;QACE,UAAU,EAAE,MAAM;QAClB,UAAU,EAAE,UAAU;QACtB,QAAQ,EAAE,CAAC,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC,CAAC;KACxD;IACD;QACE,UAAU,EAAE,MAAM;QAClB,UAAU,EAAE,UAAU;QACtB,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC,EAAE;gBAC3C,OAAO,EAAE,CAAC;gBACV,MAAM,EAAE,YAAY;gBACpB,MAAM,EAAE,QAAQ;gBAChB,cAAc,EAAE,IAAI;aACrB;SACF;KACF;IACD;QACE,UAAU,EAAE,OAAO;QACnB,UAAU,EAAE,UAAU;QACtB,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC,EAAE;gBAC3C,OAAO,EAAE,CAAC;gBACV,MAAM,EAAE,YAAY;gBACpB,MAAM,EAAE,QAAQ;gBAChB,cAAc,EAAE,IAAI;aACrB;SACF;KACF;CACF,CAAC","sourcesContent":["import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'FFT',\n    'category': 'spectral',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'IFFT',\n    'category': 'spectral',\n    'inputs': [{'start': 0, 'name': 'x', 'type': 'tensor'}]\n  },\n  {\n    'tfOpName': 'RFFT',\n    'category': 'spectral',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'}, {\n        'start': 1,\n        'name': 'fft_length',\n        'type': 'number',\n        'notSupported': true\n      }\n    ]\n  },\n  {\n    'tfOpName': 'IRFFT',\n    'category': 'spectral',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'}, {\n        'start': 1,\n        'name': 'fft_length',\n        'type': 'number',\n        'notSupported': true\n      }\n    ]\n  }\n];\n"]}
@@ -1 +1 @@
-{"version":3,"file":"transformation.js","sourceRoot":"","sources":["../../../src/operations/op_list/transformation.ts"],"names":[],"mappings":"AAEA;;;;;;;;;;;;;;;GAeG;AAEH,MAAM,CAAC,MAAM,IAAI,GAAe;IAC9B;QACE,UAAU,EAAE,MAAM;QAClB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC5C;QACD,OAAO,EAAE;YACP;gBACE,QAAQ,EAAE,MAAM;gBAChB,MAAM,EAAE,QAAQ;gBAChB,MAAM,EAAE,OAAO;gBACf,cAAc,EAAE,IAAI;aACrB;YACD,EAAC,QAAQ,EAAE,MAAM,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,OAAO,EAAC;SACrD;KACF;IACD;QACE,UAAU,EAAE,YAAY;QACxB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC/C;KACF;IACD;QACE,UAAU,EAAE,WAAW;QACvB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,SAAS,EAAE,MAAM,EAAE,UAAU,EAAC;SACpD;QACD,OAAO,EAAE,CAAC,EAAC,QAAQ,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,QAAQ,EAAC,CAAC;KAChE;IACD;QACE,UAAU,EAAE,KAAK;QACjB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,SAAS,EAAE,MAAM,EAAE,UAAU,EAAC;SACpD;QACD,OAAO,EAAE,CAAC;gBACR,QAAQ,EAAE,gBAAgB;gBAC1B,MAAM,EAAE,eAAe;gBACvB,MAAM,EAAE,QAAQ;gBAChB,cAAc,EAAE,CAAC;aAClB,CAAC;KACH;IACD;QACE,UAAU,EAAE,OAAO;QACnB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,SAAS,EAAE,MAAM,EAAE,UAAU,EAAC,EAAE;gBACnD,OAAO,EAAE,CAAC;gBACV,MAAM,EAAE,eAAe;gBACvB,MAAM,EAAE,QAAQ;gBAChB,cAAc,EAAE,CAAC;aAClB;SACF;KACF;IACD;QACE,UAAU,EAAE,SAAS;QACrB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,UAAU,EAAC;SAClD;KACF;IACD;QACE,UAAU,EAAE,SAAS;QACrB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC5C;QACD,OAAO,EAAE,CAAC;gBACR,QAAQ,EAAE,MAAM;gBAChB,kBAAkB,EAAE,cAAc;gBAClC,MAAM,EAAE,MAAM;gBACd,MAAM,EAAE,UAAU;aACnB,CAAC;KACH;IACD;QACE,UAAU,EAAE,gBAAgB;QAC5B,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,UAAU,EAAC;YACtD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,UAAU,EAAE,MAAM,EAAE,UAAU,EAAC;SACrD;KACF;IACD;QACE,UAAU,EAAE,gBAAgB;QAC5B,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,UAAU,EAAC;YACtD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,UAAU,EAAC;SAClD;KACF;IACD;QACE,UAAU,EAAE,cAAc;QAC1B,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC5C;QACD,OAAO,EAAE;YACP,EAAC,QAAQ,EAAE,YAAY,EAAE,MAAM,EAAE,WAAW,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC/D,EAAC,QAAQ,EAAE,aAAa,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,QAAQ,EAAC;SAClE;KACF;IACD;QACE,UAAU,EAAE,aAAa;QACzB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,UAAU,EAAC;SAClD;QACD,OAAO,EAAE,EAAE;KACZ;CACF,CAAC"}
+{"version":3,"file":"transformation.js","sourceRoot":"","sources":["../../../src/operations/op_list/transformation.ts"],"names":[],"mappings":"AAEA;;;;;;;;;;;;;;;GAeG;AAEH,MAAM,CAAC,MAAM,IAAI,GAAe;IAC9B;QACE,UAAU,EAAE,MAAM;QAClB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC5C;QACD,OAAO,EAAE;YACP;gBACE,QAAQ,EAAE,MAAM;gBAChB,MAAM,EAAE,QAAQ;gBAChB,MAAM,EAAE,OAAO;gBACf,cAAc,EAAE,IAAI;aACrB;YACD,EAAC,QAAQ,EAAE,MAAM,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,OAAO,EAAC;SACrD;KACF;IACD;QACE,UAAU,EAAE,YAAY;QACxB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC/C;KACF;IACD;QACE,UAAU,EAAE,WAAW;QACvB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,SAAS,EAAE,MAAM,EAAE,UAAU,EAAC;SACpD;QACD,OAAO,EAAE,CAAC,EAAC,QAAQ,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,QAAQ,EAAC,CAAC;KAChE;IACD;QACE,UAAU,EAAE,KAAK;QACjB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,SAAS,EAAE,MAAM,EAAE,UAAU,EAAC;SACpD;QACD,OAAO,EAAE,CAAC;gBACR,QAAQ,EAAE,gBAAgB;gBAC1B,MAAM,EAAE,eAAe;gBACvB,MAAM,EAAE,QAAQ;gBAChB,cAAc,EAAE,CAAC;aAClB,CAAC;KACH;IACD;QACE,UAAU,EAAE,OAAO;QACnB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,SAAS,EAAE,MAAM,EAAE,UAAU,EAAC,EAAE;gBACnD,OAAO,EAAE,CAAC;gBACV,MAAM,EAAE,eAAe;gBACvB,MAAM,EAAE,QAAQ;gBAChB,cAAc,EAAE,CAAC;aAClB;SACF;KACF;IACD;QACE,UAAU,EAAE,SAAS;QACrB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,UAAU,EAAC;SAClD;KACF;IACD;QACE,UAAU,EAAE,SAAS;QACrB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC5C;QACD,OAAO,EAAE,CAAC;gBACR,QAAQ,EAAE,MAAM;gBAChB,kBAAkB,EAAE,cAAc;gBAClC,MAAM,EAAE,MAAM;gBACd,MAAM,EAAE,UAAU;aACnB,CAAC;KACH;IACD;QACE,UAAU,EAAE,gBAAgB;QAC5B,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,UAAU,EAAC;YACtD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,UAAU,EAAE,MAAM,EAAE,UAAU,EAAC;SACrD;KACF;IACD;QACE,UAAU,EAAE,gBAAgB;QAC5B,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,UAAU,EAAC;YACtD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,UAAU,EAAC;SAClD;KACF;IACD;QACE,UAAU,EAAE,cAAc;QAC1B,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;SAC5C;QACD,OAAO,EAAE;YACP,EAAC,QAAQ,EAAE,YAAY,EAAE,MAAM,EAAE,WAAW,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC/D,EAAC,QAAQ,EAAE,aAAa,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,QAAQ,EAAC;SAClE;KACF;IACD;QACE,UAAU,EAAE,aAAa;QACzB,UAAU,EAAE,gBAAgB;QAC5B,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,GAAG,EAAE,MAAM,EAAE,QAAQ,EAAC;YAC3C,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,UAAU,EAAC;SAClD;QACD,OAAO,EAAE,EAAE;KACZ;CACF,CAAC","sourcesContent":["import {OpMapper} from '../types';\n\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const json: OpMapper[] = [\n  {\n    'tfOpName': 'Cast',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n    ],\n    'attrs': [\n      {\n        'tfName': 'SrcT',\n        'name': 'sdtype',\n        'type': 'dtype',\n        'notSupported': true\n      },\n      {'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype'}\n    ]\n  },\n  {\n    'tfOpName': 'ExpandDims',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'axis', 'type': 'number'}\n    ]\n  },\n  {\n    'tfOpName': 'MirrorPad',\n    'category': 'transformation',\n    'inputs': [\n      {'start': 0, 'name': 'x', 'type': 'tensor'},\n      {'start': 1, 'name': 'padding', 'type': 'number[]'},\n    ],\n    'attrs': [{'tfName': 'mode', 'name': 'mode', 'type': 'string'}]\n  },\n  {\n    'tfOpNameLine truncated
@@ -138,6 +138,9 @@
  "DivNoNan": [
    "divNoNan"
  ],
+  "Einsum": [
+    "einsum"
+  ],
  "Elu": [
    "elu"
  ],
@@ -218,6 +221,9 @@
  "Imag": [
    "imag"
  ],
+  "IsNan": [
+    "isNaN"
+  ],
  "LRN": [
    "localResponseNormalization"
  ],
@@ -258,6 +264,8 @@
  "LookupTableFindV2": [],
  "LookupTableImport": [],
  "LookupTableImportV2": [],
+  "LookupTableSize": [],
+  "LookupTableSizeV2": [],
  "LoopCond": [],
  "MatMul": [
    "matMul"
@@ -447,6 +455,9 @@
  "SpaceToBatchND": [
    "spaceToBatchND"
  ],
+  "SparseReshape": [
+    "sparse.sparseReshape"
+  ],
  "SparseToDense": [
    "sparseToDense",
    "cast"
@@ -290,7 +290,7 @@ export class GraphExecutor implements FunctionExecutor {
            getTensorsForCurrentContenxt(input.name, tensorMap, context);
        if (tensors != null) {
          tensors.forEach(tensor => {
-            if (tensor && !tensorsToKeep.has(tensor.id)) {
+            if (tensor && !tensor.kept && !tensorsToKeep.has(tensor.id)) {
              const count = intermediateTensorConsumerCount[tensor.id];
              if (count === 1) {
                tensor.dispose();
@@ -366,7 +366,8 @@ export class GraphExecutor implements FunctionExecutor {
    Object.keys(tensorMap).forEach(key => {
      const tensorArray = tensorMap[key];
      tensorArray.forEach(tensor => {
-        if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {
+        if (tensor && !tensor.kept && !tensor.isDisposed &&
+            !keepIds.has(tensor.id)) {
          tensor.dispose();
        }
      });
@@ -15,6 +15,8 @@
 * =============================================================================
 */
 import {DataType, keep, scalar, stack, Tensor, tidy, unstack, util} from '@tensorflow/tfjs-core';
+// tslint:disable-next-line: no-imports-from-dist
+import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';

 /**
 * Hashtable contains a set of tensors, which can be accessed by key.
@@ -59,6 +61,13 @@ export class HashTable {
    return this.tensorMap.size;
  }

+  /**
+   * The number of items in the hash table as a rank-0 tensor.
+   */
+  tensorSize(): Tensor {
+    return tfOps.scalar(this.size(), 'int32');
+  }
+
  /**
   * Replaces the contents of the table with the specified keys and values.
   * @param keys Keys to store in the hashtable.
@@ -155,7 +155,7 @@ const DYNAMIC_SHAPE_OPS = [
 ];
 const HASH_TABLE_OPS = [
  'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',
-  'LookupTableFind', 'LookupTableFindV2'
+  'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2'
 ];

 export function isControlFlow(node: Node) {
@@ -17,7 +17,7 @@

 import {concat, DataType, keep, reshape, scalar, slice, stack, Tensor, tensor, tidy, unstack} from '@tensorflow/tfjs-core';

-import {assertShapesMatchAllowUndefinedSize} from './tensor_utils';
+import {assertShapesMatchAllowUndefinedSize, inferElementShape, mergeElementShape} from './tensor_utils';

 /**
 * TensorList stores a container of `tf.Tensor` objects, which are accessible
@@ -44,13 +44,14 @@ export class TensorList {
  /**
   *
   * @param tensors list of tensors
-   * @param elementShape shape of each tensor
+   * @param elementShape shape of each tensor, this can be a single number (any
+   * shape is allowed) or partial shape (dim = -1).
   * @param elementDtype data type of each tensor
   * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1
   *   meaning that the size of `tensors` is unbounded.
   */
  constructor(
-      readonly tensors: Tensor[], readonly elementShape: number[],
+      readonly tensors: Tensor[], readonly elementShape: number|number[],
      readonly elementDtype: DataType, maxNumElements = -1) {
    if (tensors != null) {
      tensors.forEach(tensor => {
@@ -116,9 +117,11 @@ export class TensorList {
    }
    assertShapesMatchAllowUndefinedSize(
        elementShape, this.elementShape, 'TensorList shape mismatch: ');
+    const outputElementShape =
+        inferElementShape(this.elementShape, this.tensors, elementShape);
    return tidy(() => {
      const reshapedTensors =
-          this.tensors.map(tensor => reshape(tensor, elementShape));
+          this.tensors.map(tensor => reshape(tensor, outputElementShape));
      return stack(reshapedTensors, 0);
    });
  }
@@ -137,11 +140,14 @@ export class TensorList {
    if (this.size() === 0) {
      throw new Error('Trying to pop from an empty list.');
    }
-
+    const outputElementShape =
+        inferElementShape(this.elementShape, this.tensors, elementShape);
    const tensor = this.tensors.pop();
+
    assertShapesMatchAllowUndefinedSize(
        tensor.shape, elementShape, 'TensorList shape mismatch: ');
-    return reshape(tensor, elementShape);
+
+    return reshape(tensor, outputElementShape);
  }

  /**
@@ -205,8 +211,9 @@ export class TensorList {
    assertShapesMatchAllowUndefinedSize(
        this.tensors[elementIndex].shape, elementShape,
        'TensorList shape mismatch: ');
-
-    return this.tensors[elementIndex];
+    const outputElementShape =
+        inferElementShape(this.elementShape, this.tensors, elementShape);
+    return reshape(this.tensors[elementIndex], outputElementShape);
  }

  /**
@@ -252,13 +259,15 @@ export class TensorList {
    // When indices is greater than the size of the list, indices beyond the
    // size of the list are ignored.
    indices = indices.slice(0, this.size());
-
+    const outputElementShape =
+        inferElementShape(this.elementShape, this.tensors, elementShape);
    if (indices.length === 0) {
-      return tensor([], [0].concat(this.elementShape));
+      return tensor([], [0].concat(outputElementShape));
    }

    return tidy(() => {
-      const tensors = indices.map(i => reshape(this.tensors[i], elementShape));
+      const tensors =
+          indices.map(i => reshape(this.tensors[i], outputElementShape));
      return stack(tensors, 0);
    });
  }
@@ -276,13 +285,14 @@ export class TensorList {

    assertShapesMatchAllowUndefinedSize(
        this.elementShape, elementShape, 'TensorList shape mismatch: ');
+    const outputElementShape =
+        inferElementShape(this.elementShape, this.tensors, elementShape);

    if (this.size() === 0) {
-      return tensor([], [0].concat(this.elementShape));
+      return tensor([], [0].concat(outputElementShape));
    }
-
    return tidy(() => {
-      const tensors = this.tensors.map(t => reshape(t, elementShape));
+      const tensors = this.tensors.map(t => reshape(t, outputElementShape));
      return concat(tensors, 0);
    });
  }
@@ -304,10 +314,9 @@ export function fromTensor(
    throw new Error(`Invalid data types; op elements ${
        tensor.dtype}, but list elements ${elementDtype}`);
  }
-  const outputShape = tensor.shape.slice(1);
+  const tensorElementShape = tensor.shape.slice(1);
  assertShapesMatchAllowUndefinedSize(
-      outputShape, elementShape, 'TensorList shape mismatch: ');
-
+      tensorElementShape, elementShape, 'TensorList shape mismatch: ');
  const tensorList: Tensor[] = unstack(tensor);
  return new TensorList(tensorList, elementShape, dtype);
 }
@@ -374,6 +383,9 @@ export function split(
        ${totalLength}, and tensor's shape is: ${tensor.shape}`);
  }

+  const shapeWithoutFirstDim = tensor.shape.slice(1);
+  const outputElementShape =
+      mergeElementShape(shapeWithoutFirstDim, elementShape);
  const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
  const tensors: Tensor[] = tidy(() => {
    const tensors = [];
@@ -382,7 +394,8 @@ export function split(
      const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];
      const indices = [0, previousLength, 0];
      const sizes = [1, length[i], elementPerRow];
-      tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);
+      tensors[i] = reshape(
+          slice(tensor, indices, sizes), outputElementShape as number[]);
    }
    tensor.dispose();
    return tensors;
@@ -21,23 +21,93 @@
 * anything.
 */

-import {util} from '@tensorflow/tfjs-core';
+import {Tensor, util} from '@tensorflow/tfjs-core';

+/**
+ * Used by TensorList and TensorArray to verify if elementShape matches, support
+ * negative value as the dim shape.
+ * @param shapeA
+ * @param shapeB
+ * @param errorMessagePrefix
+ */
 export function assertShapesMatchAllowUndefinedSize(
-    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {
+    shapeA: number|number[], shapeB: number|number[],
+    errorMessagePrefix = ''): void {
+  // constant shape means unknown rank
+  if (typeof shapeA === 'number' || typeof shapeB === 'number') {
+    return;
+  }
  util.assert(
-      shapesEqualAllowUndefinedSize(shapeA, shapeB),
+      shapeA.length === shapeB.length,
      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+  for (let i = 0; i < shapeA.length; i++) {
+    const dim0 = shapeA[i];
+    const dim1 = shapeB[i];
+    util.assert(
+        dim0 < 0 || dim1 < 0 || dim0 === dim1,
+        () =>
+            errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+  }
 }

-export function shapesEqualAllowUndefinedSize(n1: number[], n2: number[]) {
-  if (n1.length !== n2.length) {
+export function fullDefinedShape(elementShape: number|number[]): boolean {
+  if (typeof elementShape === 'number' || elementShape.some(dim => dim < 0)) {
    return false;
  }
-  for (let i = 0; i < n1.length; i++) {
-    if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {
-      return false;
-    }
-  }
  return true;
 }
+/**
+ * Generate the output element shape from the list elementShape, list tensors
+ * and input param.
+ * @param listElementShape
+ * @param tensors
+ * @param elementShape
+ */
+export function inferElementShape(
+    listElementShape: number|number[], tensors: Tensor[],
+    elementShape: number|number[]): number[] {
+  let partialShape = mergeElementShape(listElementShape, elementShape);
+  const notfullDefinedShape = !fullDefinedShape(partialShape);
+  if (notfullDefinedShape && tensors.length === 0) {
+    throw new Error(
+        `Tried to calculate elements of an empty list` +
+        ` with non-fully-defined elementShape: ${partialShape}`);
+  }
+  if (notfullDefinedShape) {
+    tensors.forEach(tensor => {
+      partialShape = mergeElementShape(tensor.shape, partialShape);
+    });
+  }
+  if (!fullDefinedShape(partialShape)) {
+    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);
+  }
+  return partialShape as number[];
+}
+
+export function mergeElementShape(
+    elementShapeA: number|number[], elementShapeB: number|number[]): number|
+    number[] {
+  if (typeof elementShapeA === 'number') {
+    return elementShapeB;
+  }
+  if (typeof elementShapeB === 'number') {
+    return elementShapeA;
+  }
+
+  if (elementShapeA.length !== elementShapeB.length) {
+    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${
+        elementShapeB}`);
+  }
+
+  const result: number[] = [];
+  for (let i = 0; i < elementShapeA.length; ++i) {
+    const dim0 = elementShapeA[i];
+    const dim1 = elementShapeB[i];
+    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
+      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${
+          elementShapeB}`);
+    }
+    result[i] = dim0 >= 0 ? dim0 : dim1;
+  }
+  return result;
+}
@@ -172,6 +172,9 @@ export const executeOp: InternalOpExecutor =
          return [tfOps.prelu(
              getParamValue('x', node, tensorMap, context) as Tensor,
              getParamValue('alpha', node, tensorMap, context) as Tensor)];
+        case 'IsNan':
+          return [tfOps.isNaN(
+              getTensor(node.inputNames[0], tensorMap, context))];
        default:
          throw TypeError(`Node type ${node.op} is not implemented`);
      }
@@ -50,7 +50,7 @@ function fusedConvAndDepthWiseParams(
  }
  if (isBatchNorm) {
    throw new Error(
-        'FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');
+        'FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');
  }
  const stride = getParamValue('strides', node, tensorMap, context) as number[];
  const pad = getPadding(node, tensorMap, context);
@@ -65,6 +65,15 @@ export const executeOp: InternalOpAsyncExecutor = async(
      const hashTable = resourceManager.getHashTableById(handle.id);
      return [await hashTable.find(keys, defaultValue)];
    }
+    case 'LookupTableSize':
+    case 'LookupTableSizeV2': {
+      const handle = getParamValue(
+                         'tableHandle', node, tensorMap, context,
+                         resourceManager) as Tensor;
+
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [hashTable.tensorSize()];
+    }
    default:
      throw TypeError(`Node type ${node.op} is not implemented`);
  }
@@ -39,6 +39,12 @@ export const executeOp: InternalOpExecutor =
              getParamValue('transposeB', node, tensorMap, context) as
                  boolean)];

+        case 'Einsum':
+          return [tfOps.einsum(
+              getParamValue('equation', node, tensorMap, context) as string,
+              ...getParamValue('tensors', node, tensorMap, context) as
+                  Tensor[])];
+
        case 'Transpose':
          return [tfOps.transpose(
              getParamValue('x', node, tensorMap, context) as Tensor,
@@ -0,0 +1,45 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import {Tensor, Tensor1D, Tensor2D} from '@tensorflow/tfjs-core';
+// tslint:disable-next-line: no-imports-from-dist
+import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
+
+import {NamedTensorsMap} from '../../data/types';
+import {ExecutionContext} from '../../executor/execution_context';
+import {InternalOpExecutor, Node} from '../types';
+
+import {getParamValue} from './utils';
+
+export const executeOp: InternalOpExecutor =
+    (node: Node, tensorMap: NamedTensorsMap,
+     context: ExecutionContext): Tensor[] => {
+      switch (node.op) {
+        case 'SparseReshape': {
+          const {outputIndices, outputShape} = tfOps.sparse.sparseReshape(
+              getParamValue('inputIndices', node, tensorMap, context) as
+                  Tensor2D,
+              getParamValue('inputShape', node, tensorMap, context) as Tensor1D,
+              getParamValue('newShape', node, tensorMap, context) as Tensor1D);
+          return [outputIndices, outputShape];
+        }
+        default:
+          throw TypeError(`Node type ${node.op} is not implemented`);
+      }
+    };
+
+export const CATEGORY = 'convolution';
@@ -463,12 +463,20 @@ export const json: OpMapper[] = [
        'type': 'number',
        'defaultValue': 0.2
      },
-      {
-        'tfName': 'T',
-        'name': 'dtype',
-        'type': 'dtype',
-        'notSupported': true
-      }
+      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}
    ]
+  },
+  {
+    'tfOpName': 'IsNan',
+    'category': 'basic_math',
+    'inputs': [
+      {'start': 0, 'name': 'x', 'type': 'tensor'},
+    ],
+    'attrs': [{
+      'tfName': 'T',
+      'name': 'dtype',
+      'type': 'dtype',
+      'notSupported': true
+    }]
  }
 ];
@@ -114,5 +114,19 @@ export const json: OpMapper[] = [
        'notSupported': true
      }
    ]
+  },
+  {
+    'tfOpName': 'LookupTableSize',
+    'category': 'hash_table',
+    'inputs': [
+      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'}
+    ]
+  },
+  {
+    'tfOpName': 'LookupTableSizeV2',
+    'category': 'hash_table',
+    'inputs': [
+      {'start': 0, 'name': 'tableHandle', 'type': 'tensor'}
+    ]
  }
 ];
@@ -130,11 +130,18 @@ export const json: OpMapper[] = [
      {'start': 0, 'name': 'x', 'type': 'tensor'},
      {'start': 1, 'name': 'perm', 'type': 'number[]'},
    ],
-    'attrs': [{
-      'tfName': 'T',
-      'name': 'dtype',
-      'type': 'dtype',
-      'notSupported': true
-    }]
+    'attrs': [
+      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}
+    ]
+  },
+  {
+    'tfOpName': 'Einsum',
+    'category': 'matrices',
+    'inputs': [{'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors'}],
+    'attrs': [
+      {'tfName': 'equation', 'name': 'equation', 'type': 'string'},
+      {'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2},
+      {'tfName': 'T', 'name': 'dtype', 'type': 'dtype'}
+    ]
  }
 ];
@@ -0,0 +1,30 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import {OpMapper} from '../types';
+
+export const json: OpMapper[] = [{
+  'tfOpName': 'SparseReshape',
+  'category': 'sparse',
+  'inputs': [
+    {'start': 0, 'name': 'inputIndices', 'type': 'tensor'},
+    {'start': 1, 'name': 'inputShape', 'type': 'tensor'},
+    {'start': 2, 'name': 'newShape', 'type': 'tensor'},
+  ],
+  'attrs':
+      [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}]
+}];
@@ -42,7 +42,7 @@ export const json = {
        'arithmetic', 'basic_math', 'control', 'convolution', 'custom',
        'dynamic', 'evaluation', 'image', 'creation', 'graph', 'logical',
        'matrices', 'normalization', 'reduction', 'slice_join', 'spectral',
-        'transformation'
+        'transformation', 'sparse'
      ]
    },
    'InputParamMapper': {
@@ -54,8 +54,7 @@ export const json = {
          'anyOf': [
            {'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}},
            {'type': 'number'}, {'type': 'array', 'items': {'type': 'number'}},
-            {'type': 'boolean'},
-            {'type': 'array', 'items': {'type': 'boolean'}}
+            {'type': 'boolean'}, {'type': 'array', 'items': {'type': 'boolean'}}
          ]
        },
        'notSupported': {'type': 'boolean'},
@@ -81,8 +80,7 @@ export const json = {
          'anyOf': [
            {'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}},
            {'type': 'number'}, {'type': 'array', 'items': {'type': 'number'}},
-            {'type': 'boolean'},
-            {'type': 'array', 'items': {'type': 'boolean'}}
+            {'type': 'boolean'}, {'type': 'array', 'items': {'type': 'boolean'}}
          ]
        },
        'notSupported': {'type': 'boolean'},
@@ -38,6 +38,7 @@ import * as matrices from './executors/matrices_executor';
 import * as normalization from './executors/normalization_executor';
 import * as reduction from './executors/reduction_executor';
 import * as sliceJoin from './executors/slice_join_executor';
+import * as sparse from './executors/sparse_executor';
 import * as spectral from './executors/spectral_executor';
 import * as transformation from './executors/transformation_executor';
 import {Node} from './types';
@@ -90,6 +91,8 @@ export function executeOp(
          case 'slice_join':
            return tfc.tidy(
                () => sliceJoin.executeOp(node, tensorMap, context));
+          case 'sparse':
+            return tfc.tidy(() => sparse.executeOp(node, tensorMap, context));
          case 'spectral':
            return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));
          case 'transformation':
@@ -26,7 +26,7 @@ export type ParamType = 'number'|'string'|'string[]'|'number[]'|'bool'|'bool[]'|
 export type Category = 'arithmetic'|'basic_math'|'control'|'convolution'|
    'custom'|'dynamic'|'evaluation'|'image'|'creation'|'graph'|'logical'|
    'matrices'|'normalization'|'reduction'|'slice_join'|'spectral'|
-    'transformation'|'hash_table';
+    'transformation'|'hash_table'|'sparse';

 // For mapping input or attributes of NodeDef into TensorFlow.js op param.
 export declare interface ParamMapper {
@@ -1,5 +1,5 @@
 /** @license See the LICENSE file. */

 // This code is auto-generated, do not modify this file!
-const version = '3.0.0';
+const version = '3.6.0';
 export {version};
@@ -1 +0,0 @@
-node_modules
@@ -1,2 +1,152 @@
+# Copyright 2021 Google LLC. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+load("@build_bazel_rules_nodejs//:index.bzl", "js_library")
+load("@npm//@bazel/concatjs:index.bzl", "karma_web_test")
+load("@npm//@bazel/typescript:index.bzl", "ts_library", "ts_project")
+load("//:esbuild.bzl", "esbuild")
+
+package(default_visibility = ["//visibility:public"])
+
 # Allow typescript rules in any package to reference this file
 exports_files(["tsconfig.json"])
+
+TEST_SRCS = ["src/setup_test.ts"]
+
+ts_project(
+    name = "tfjs-core_lib",
+    srcs = glob(
+        ["src/**/*.ts"],
+        exclude = TEST_SRCS,
+    ),
+    declaration = True,
+    extends = "//:tsconfig.json",
+    incremental = True,
+    out_dir = "dist",
+    root_dir = "src",
+    source_map = True,
+    tsconfig = "tsconfig.json",
+    deps = [
+        "@npm//@types",
+        "@npm//jasmine-core",
+        "@npm//seedrandom",
+    ],
+)
+
+ts_library(
+    name = "tfjs-core_test_lib",
+    testonly = True,
+    srcs = [
+        "setup_test_bazel.ts",
+    ],
+    tsconfig = "//:tsconfig_ts_library.json",
+    deps = [
+        ":tfjs-core",
+        "@npm//@types",
+        "@npm//jasmine-core",
+        "@npm//seedrandom",
+    ],
+)
+
+esbuild(
+    name = "tfjs-core_test_bundle",
+    testonly = True,
+    entry_point = "setup_test_bazel.ts",
+    external = [
+        # webworker tests call 'require('@tensorflow/tfjs')', which
+        # is external to the test bundle.
+        # Note: This is not a bazel target. It's just a string.
+        "@tensorflow/tfjs",
+    ],
+    sources_content = True,
+    deps = [
+        ":tfjs-core_lib",
+        ":tfjs-core_test_lib",
+        "//tfjs-backend-cpu",
+    ],
+)
+
+esbuild(
+    name = "tf-core.min",
+    entry_point = "dist/index.ts",
+    external = [
+        "node-fetch",
+        "util",
+    ],
+    minify = True,
+    sources_content = True,
+    deps = [
+        ":tfjs-core_lib",
+    ],
+)
+
+esbuild(
+    name = "tf-core.node",
+    entry_point = "dist/index.ts",
+    external = [
+        "node-fetch",
+        "util",
+    ],
+    sources_content = True,
+    deps = [
+        ":tfjs-core_lib",
+    ],
+)
+
+js_library(
+    name = "tfjs-core",
+    package_name = "@tensorflow/tfjs-core",
+    srcs = [
+        "package.json",
+    ],
+    deps = [
+        ":tf-core.min",
+        ":tf-core.node",
+        ":tfjs-core_lib",
+    ],
+)
+
+karma_web_test(
+    name = "tfjs-core_test",
+    srcs = [
+        ":tfjs-core_test_bundle",
+    ],
+    static_files = [
+        # Listed here so sourcemaps are served
+        ":tfjs-core_test_bundle",
+    ],
+    tags = ["native"],
+)
+
+karma_web_test(
+    name = "browserstack_tfjs-core_test",
+    srcs = [
+        ":tfjs-core_test_bundle",
+    ],
+    config_file = "//:karma.conf.js",
+    peer_deps = [
+        "@npm//karma",
+        "@npm//karma-jasmine",
+        "@npm//karma-requirejs",
+        "@npm//karma-sourcemap-loader",
+        "@npm//requirejs",
+        "@npm//karma-browserstack-launcher",
+    ],
+    static_files = [
+        # Listed here so sourcemaps are served
+        ":tfjs-core_test_bundle",
+    ],
+    tags = ["ci"],
+)
@@ -27,14 +27,15 @@ export interface BackendTimingInfo {
 export interface TensorStorage {
    read(dataId: DataId): Promise<BackendValues>;
    readSync(dataId: DataId): BackendValues;
-    disposeData(dataId: DataId): void;
+    disposeData(dataId: DataId, force?: boolean): boolean;
    write(values: BackendValues, shape: number[], dtype: DataType): DataId;
-    move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType): void;
+    move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType, refCount: number): void;
    memory(): {
        unreliable: boolean;
    };
    /** Returns number of data ids currently in the storage. */
    numDataIds(): number;
+    refCount(dataId: DataId): number;
 }
 /** Convenient class for storing tensor-related data. */
 export declare class DataStorage<T> {
@@ -58,6 +59,7 @@ export interface DataMover {
    moveData(backend: KernelBackend, dataId: DataId): void;
 }
 export interface BackendTimer {
+    timerAvailable(): boolean;
    time(f: () => void): Promise<BackendTimingInfo>;
 }
 /**
@@ -67,21 +69,16 @@ export interface BackendTimer {
 * methods).
 */
 export declare class KernelBackend implements TensorStorage, Backend, BackendTimer {
-    /**
-     * Decrease the complex ref count for the dataId, this is useful for WebGL
-     * backend to keep the real and imag components of the complex tensor in sync
-     * with the engine. WASM and node do not have internal ref count, they will
-     * use on the default implementation.
-     * @param dataId
-     */
-    decComplexRef(dataId: DataId): void;
+    refCount(dataId: DataId): number;
+    incRef(dataId: DataId): void;
+    timerAvailable(): boolean;
    time(f: () => void): Promise<BackendTimingInfo>;
    read(dataId: object): Promise<BackendValues>;
    readSync(dataId: object): BackendValues;
    numDataIds(): number;
-    disposeData(dataId: object): void;
+    disposeData(dataId: object, force?: boolean): boolean;
    write(values: BackendValues, shape: number[], dtype: DataType): DataId;
-    move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType): void;
+    move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType, refCount: number): void;
    memory(): {
        unreliable: boolean;
        reasons?: string[];
@@ -52,15 +52,14 @@ export class DataStorage {
 * methods).
 */
 export class KernelBackend {
-    /**
-     * Decrease the complex ref count for the dataId, this is useful for WebGL
-     * backend to keep the real and imag components of the complex tensor in sync
-     * with the engine. WASM and node do not have internal ref count, they will
-     * use on the default implementation.
-     * @param dataId
-     */
-    decComplexRef(dataId) {
-        return;
+    refCount(dataId) {
+        return notYetImplemented('refCount');
+    }
+    incRef(dataId) {
+        return notYetImplemented('incRef');
+    }
+    timerAvailable() {
+        return true;
    }
    time(f) {
        return notYetImplemented('time');
@@ -74,13 +73,13 @@ export class KernelBackend {
    numDataIds() {
        return notYetImplemented('numDataIds');
    }
-    disposeData(dataId) {
+    disposeData(dataId, force) {
        return notYetImplemented('disposeData');
    }
    write(values, shape, dtype) {
        return notYetImplemented('write');
    }
-    move(dataId, values, shape, dtype) {
+    move(dataId, values, shape, dtype, refCount) {
        return notYetImplemented('move');
    }
    memory() {
@@ -1 +1 @@
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+{"version":3,"file":"backend.js","sourceRoot":"","sources":["../../src/backends/backend.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAKH,MAAM,CAAC,MAAM,eAAe,GAAG,IAAI,CAAC;AACpC,MAAM,CAAC,MAAM,eAAe,GAAG,IAAI,CAAC;AAuBpC,wDAAwD;AACxD,MAAM,OAAO,WAAW;IAItB,YAAoB,OAAsB,EAAU,SAAoB;QAApD,YAAO,GAAP,OAAO,CAAe;QAAU,cAAS,GAAT,SAAS,CAAW;QAHhE,SAAI,GAAG,IAAI,OAAO,EAAa,CAAC;QAChC,iBAAY,GAAG,CAAC,CAAC;IAEkD,CAAC;IAE5E,GAAG,CAAC,MAAc;QAChB,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,EAAE;YAC1B,IAAI,CAAC,SAAS,CAAC,QAAQ,CAAC,IAAI,CAAC,OAAO,EAAE,MAAM,CAAC,CAAC;SAC/C;QACD,OAAO,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC;IAC/B,CAAC;IAED,GAAG,CAAC,MAAc,EAAE,KAAQ;QAC1B,IAAI,CAAC,YAAY,EAAE,CAAC;QACpB,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC;IAC/B,CAAC;IAED,GAAG,CAAC,MAAc;QAChB,OAAO,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC;IAC/B,CAAC;IAED,MAAM,CAAC,MAAc;QACnB,IAAI,CAAC,YAAY,EAAE,CAAC;QACpB,OAAO,IAAI,CAAC,IAAI,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC;IAClC,CAAC;IAED,UAAU;QACR,OAAO,IAAI,CAAC,YAAY,CAAC;IAC3B,CAAC;CACF;AAiBD;;;;;GAKG;AACH,MAAM,OAAO,aAAa;IACxB,QAAQ,CAAC,MAAc;QACrB,OAAO,iBAAiB,CAAC,UAAU,CAAC,CAAC;IACvC,CAAC;IACD,MAAM,CAAC,MAAc;QACnB,OAAO,iBAAiB,CAAC,QAAQ,CAAC,CAAC;IACrC,CAAC;IACD,cAAc;QACZ,OAAO,IAAI,CAAC;IACd,CAAC;IACD,IAAI,CAAC,CAAa;QAChB,OAAO,iBAAiB,CAAC,MAAM,CAAC,CAAC;IACnC,CAAC;IACD,IAAI,CAAC,MAAc;QACjB,OAAO,iBAAiB,CAAC,MAAM,CAAC,CAAC;IACnC,CAAC;IACD,QAAQ,CAAC,MAAc;QACrB,OAAO,iBAAiB,CAAC,UAAU,CAAC,CAAC;IACvC,CAAC;IACD,UAAU;QACR,OAAO,iBAAiB,CAAC,YAAY,CAAC,CAAC;IACzC,CAAC;IACD,WAAW,CAAC,MAAc,EAAE,KAAe;QACzC,OAAO,iBAAiB,CAAC,aAAa,CAAC,CAAC;IAC1C,CAAC;IACD,KAAK,CAAC,MAAqB,EAAE,KAAe,EAAE,KAAe;QAC3D,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAC;IACpC,CAAC;IACD,IAAI,CACA,MAAc,EAAE,MAAqB,EAAE,KAAe,EAAE,KAAe,EACvE,QAAgB;QAClB,OAAO,iBAAiB,CAAC,MAAM,CAAC,CAAC;IACnC,CAAC;IACD,MAAM;QACJ,OAAO,iBAAiB,CAAC,QAAQ,CAAC,CAAC;IACrC,CAAC;IACD,uEAAuE;IACvE,cAAc;QACZ,OAAO,iBAAiB,CAAC,gBAAgB,CAAC,CAAC;IAC7C,CAAC;IACD,kDAAkD;IAClD,OAAO;QACL,OAAO,IAAI,CAAC,cAAc,EAAE,KAAK,EAAE,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,eAAe,CAAC;IAC1E,CAAC;IACD,OAAO;QACL,OAAO,iBAAiB,CAAC,SAAS,CAAC,CAAC;IACtC,CAAC;CACF;AAED,SAAS,iBAAiB,CAAC,UAAkB;IAC3C,MAAM,IAAI,KAAK,CACX,IAAI,UAAU,sDAAsD;QACpE,sEAAsE,CAAC,CAAC;AAC9E,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Backend, DataId} from '../tensor';\nimport {BackendValues, DataType} from '../types';\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number|{error: string};\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<BackendValues>;\n  readSync(dataId: DataId): BackendValues;\n  disposeData(dataId: DataId, force?: boolean): boolean;\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId;\n  move(\n      dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n      refCount: number): void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n  /** Returns number of data ids currently in the storage. */\n  numDataIds(): number;\n  refCount(dataId: DataId): number;\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n  private dataIdsCount = 0;\n\n  constructor(private backend: KernelBackend, private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n\n  numDataIds(): number {\n    return this.dataIdsCount;\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the currLine truncated
@@ -1 +1 @@
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+{"version":3,"file":"backend_test.js","sourceRoot":"","sources":["../../src/backends/backend_test.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,KAAK,EAAE,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,QAAQ,EAAE,iBAAiB,EAAC,MAAM,iBAAiB,CAAC;AAC5D,OAAO,EAAC,eAAe,EAAE,eAAe,EAAC,MAAM,WAAW,CAAC;AAE3D,iBAAiB,CAAC,SAAS,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC1C,EAAE,CAAC,0CAA0C,EAAE,GAAG,EAAE;QAClD,MAAM,YAAY,GAAG,EAAE,CAAC,OAAO,EAAE,CAAC,cAAc,EAAE,KAAK,EAAE,CAAC,CAAC;YACvD,eAAe,CAAC,CAAC;YACjB,eAAe,CAAC;QACpB,MAAM,CAAC,EAAE,CAAC,OAAO,EAAE,CAAC,OAAO,EAAE,CAAC,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;IACpD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kBAAkB,EAAE,KAAK,IAAI,EAAE;QAChC,MAAM,CAAC,MAAM,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,OAAO,EAAE,CAAC,OAAO,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC;IAC1E,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {EPSILON_FLOAT16, EPSILON_FLOAT32} from './backend';\n\ndescribeWithFlags('epsilon', ALL_ENVS, () => {\n  it('Epsilon is a function of float precision', () => {\n    const epsilonValue = tf.backend().floatPrecision() === 32 ?\n        EPSILON_FLOAT32 :\n        EPSILON_FLOAT16;\n    expect(tf.backend().epsilon()).toBe(epsilonValue);\n  });\n\n  it('abs(epsilon) > 0', async () => {\n    expect(await tf.abs(tf.backend().epsilon()).array()).toBeGreaterThan(0);\n  });\n});\n"]}
@@ -34,6 +34,7 @@ export * from '../ops/fused_util';
 export * from '../ops/erf_util';
 export * from '../log';
 export * from '../backends/complex_util';
+export * from '../backends/einsum_util';
 export * from '../ops/split_util';
 import * as segment_util from '../ops/segment_util';
 export { segment_util };
@@ -34,6 +34,7 @@ export * from '../ops/fused_util';
 export * from '../ops/erf_util';
 export * from '../log';
 export * from '../backends/complex_util';
+export * from '../backends/einsum_util';
 export * from '../ops/split_util';
 import * as segment_util from '../ops/segment_util';
 export { segment_util };
@@ -1 +1 @@
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArraLine truncated
@@ -1 +1 @@
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\nimport * as complex_util from './complex_util';\n\ndescribe('complex_util', () => {\n  it('mergeRealAndImagArrays', () => {\n    const real = new Float32Array([1, 2, 3]);\n    const imag = new Float32Array([4, 5, 6]);\n    const complex = complex_util.mergeRealAndImagArrays(real, imag);\n    expect(complex).toEqual(new Float32Array([1, 4, 2, 5, 3, 6]));\n  });\n\n  it('splitRealAndImagArrays', () => {\n    const complex = new Float32Array([1, 4, 2, 5, 3, 6]);\n    const result = complex_util.splitRealAndImagArrays(complex);\n    expect(result.real).toEqual(new Float32Array([1, 2, 3]));\n    expect(result.imag).toEqual(new Float32Array([4, 5, 6]));\n  });\n\n  it('complexWithEvenIndex', () => {\n    const complex = new Float32Array([1, 2, 3, 4, 5, 6]);\n    const result = complex_util.complexWithEvenIndex(complex);\n    expect(result.real).toEquaLine truncated
@@ -0,0 +1,80 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * Utility functions for computing einsum (tensor contraction and summation
+ * based on Einstein summation.)
+ */
+import { Tensor } from '../tensor';
+/**
+ * Parse an equation for einsum.
+ *
+ * @param equation The einsum equation (e.g., "ij,jk->ik").
+ * @param numTensors Number of tensors provided along with `equation`. Used to
+ *   check matching number of input tensors.
+ * @returns An object consisting of the following fields:
+ *   - allDims: all dimension names as strings.
+ *   - summedDims: a list of all dimensions being summed over, as indices to
+ *     the elements of `allDims`.
+ *   - idDims: indices of the dimensions in each input tensor, as indices to
+ *     the elements of `allDims.
+ */
+export declare function decodeEinsumEquation(equation: string, numTensors: number): {
+    allDims: string[];
+    summedDims: number[];
+    idDims: number[][];
+};
+/**
+ * Get the permutation for a given input tensor.
+ *
+ * @param nDims Total number of dimension of all tensors involved in the einsum
+ *   operation.
+ * @param idDims Dimension indices involve in the tensor in question.
+ * @returns An object consisting of the following fields:
+ *   - permutationIndices: Indices to permute the axes of the tensor with.
+ *   - expandDims: Indices to the dimension that need to be expanded from the
+ *     tensor after permutation.
+ */
+export declare function getEinsumPermutation(nDims: number, idDims: number[]): {
+    permutationIndices: number[];
+    expandDims: number[];
+};
+/**
+ * Checks that the dimension sizes from different input tensors match the
+ * equation.
+ */
+export declare function checkEinsumDimSizes(nDims: number, idDims: number[][], tensors: Tensor[]): void;
+/**
+ * Gets path of computation for einsum.
+ *
+ * @param summedDims indices to the dimensions being summed over.
+ * @param idDims A look up table for the dimensions present in each input
+ *     tensor. Each consituent array contains indices for the dimensions in the
+ *     corresponding input tensor.
+ *
+ * @return A map with two fields:
+ *   - path: The path of computation, with each element indicating the dimension
+ *     being summed over after the element-wise multiplication in that step.
+ *   - steps: With the same length as `path`. Each element contains the indices
+ *     to the input tensors being used for element-wise multiplication in the
+ *     corresponding step.
+ */
+export declare function getEinsumComputePath(summedDims: number[], idDims: number[][]): {
+    path: number[];
+    steps: number[][];
+};
+/** Determines if an axes permutation is the identity permutation. */
+export declare function isIdentityPermutation(perm: number[]): boolean;
@@ -0,0 +1,190 @@
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+import { assert } from '../util_base';
+const ARROW = '->';
+const ARROW_REGEX = /->/g;
+const COMMA = ',';
+const ELLIPSIS = '...';
+/**
+ * Parse an equation for einsum.
+ *
+ * @param equation The einsum equation (e.g., "ij,jk->ik").
+ * @param numTensors Number of tensors provided along with `equation`. Used to
+ *   check matching number of input tensors.
+ * @returns An object consisting of the following fields:
+ *   - allDims: all dimension names as strings.
+ *   - summedDims: a list of all dimensions being summed over, as indices to
+ *     the elements of `allDims`.
+ *   - idDims: indices of the dimensions in each input tensor, as indices to
+ *     the elements of `allDims.
+ */
+export function decodeEinsumEquation(equation, numTensors) {
+    equation = equation.replace(/\s/g, ''); // Remove witespace in equation.
+    const numArrows = (equation.length - equation.replace(ARROW_REGEX, '').length) /
+        ARROW.length;
+    if (numArrows < 1) {
+        throw new Error('Equations without an arrow are not supported.');
+    }
+    else if (numArrows > 1) {
+        throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`);
+    }
+    const [inputString, outputString] = equation.split(ARROW);
+    assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`);
+    const inputTerms = inputString.split(COMMA);
+    const numInputs = inputTerms.length;
+    if (numTensors !== numInputs) {
+        throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);
+    }
+    if (numInputs > 2) {
+        throw new Error('Support for more than 2 input tensors is not implemented yet.');
+    }
+    const allDims = [];
+    for (let i = 0; i < outputString.length; ++i) {
+        const dimName = outputString[i];
+        if (!inputTerms.some(inputTerm => inputTerm.indexOf(dimName) !== -1)) {
+            throw new Error(`Output subscripts contain the label ${dimName} ` +
+                `not present in the input subscripts.`);
+        }
+        if (allDims.indexOf(dimName) === -1) {
+            allDims.push(dimName);
+        }
+    }
+    for (let i = 0; i < inputString.length; ++i) {
+        const dimName = inputString[i];
+        if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
+            allDims.push(dimName);
+        }
+    }
+    const idDims = new Array(inputTerms.length);
+    for (let i = 0; i < numInputs; ++i) {
+        if (new Set(inputTerms[i].split('')).size !== inputTerms[i].length) {
+            throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. ` +
+                `Support for duplicate axes in input is not implemented yet.`);
+        }
+        idDims[i] = [];
+        for (let j = 0; j < inputTerms[i].length; ++j) {
+            idDims[i].push(allDims.indexOf(inputTerms[i][j]));
+        }
+    }
+    const numDims = allDims.length; // Number of unique dimensions.
+    const numOutDims = outputString.length; // Number of output dimensions.
+    const summedDims = []; // Dimensions being summed over.
+    for (let i = numOutDims; i < numDims; ++i) {
+        summedDims.push(i);
+    }
+    return { allDims, summedDims, idDims };
+}
+/**
+ * Get the permutation for a given input tensor.
+ *
+ * @param nDims Total number of dimension of all tensors involved in the einsum
+ *   operation.
+ * @param idDims Dimension indices involve in the tensor in question.
+ * @returns An object consisting of the following fields:
+ *   - permutationIndices: Indices to permute the axes of the tensor with.
+ *   - expandDims: Indices to the dimension that need to be expanded from the
+ *     tensor after permutation.
+ */
+export function getEinsumPermutation(nDims, idDims) {
+    let permutationIndices = new Array(nDims);
+    permutationIndices.fill(-1);
+    for (let i = 0; i < idDims.length; ++i) {
+        permutationIndices[idDims[i]] = i;
+    }
+    const expandDims = [];
+    for (let i = 0; i < nDims; ++i) {
+        if (permutationIndices[i] === -1) {
+            expandDims.push(i);
+        }
+    }
+    permutationIndices = permutationIndices.filter(d => d !== -1);
+    return { permutationIndices, expandDims };
+}
+/**
+ * Checks that the dimension sizes from different input tensors match the
+ * equation.
+ */
+export function checkEinsumDimSizes(nDims, idDims, tensors) {
+    const dimSizes = new Array(nDims);
+    for (let i = 0; i < tensors.length; ++i) {
+        const shape = tensors[i].shape;
+        for (let j = 0; j < idDims[i].length; ++j) {
+            if (dimSizes[idDims[i][j]] === undefined) {
+                dimSizes[idDims[i][j]] = shape[j];
+            }
+            else {
+                assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} ` +
+                    `of input shaped ${JSON.stringify(shape)}, ` +
+                    `but got dimension ${shape[j]}`);
+            }
+        }
+    }
+}
+/**
+ * Gets path of computation for einsum.
+ *
+ * @param summedDims indices to the dimensions being summed over.
+ * @param idDims A look up table for the dimensions present in each input
+ *     tensor. Each consituent array contains indices for the dimensions in the
+ *     corresponding input tensor.
+ *
+ * @return A map with two fields:
+ *   - path: The path of computation, with each element indicating the dimension
+ *     being summed over after the element-wise multiplication in that step.
+ *   - steps: With the same length as `path`. Each element contains the indices
+ *     to the input tensors being used for element-wise multiplication in the
+ *     corresponding step.
+ */
+export function getEinsumComputePath(summedDims, idDims) {
+    const path = summedDims;
+    const steps = [];
+    let nSteps = 0;
+    if (summedDims.length === 0) {
+        // Einsum that involes no summing: e.g., transpose and outer product.
+        path.push(-1);
+    }
+    nSteps = summedDims.length + 1;
+    for (let i = 0; i < nSteps; ++i) {
+        steps.push([]);
+    }
+    const computedTermIndices = [];
+    for (let i = 0; i < path.length; ++i) {
+        const summedDim = path[i];
+        const termIndices = findTermsWithDim(idDims, summedDim);
+        for (const termIndex of termIndices) {
+            if (computedTermIndices.indexOf(termIndex) === -1) {
+                steps[i].push(termIndex);
+                computedTermIndices.push(termIndex);
+            }
+        }
+    }
+    return { path, steps };
+}
+/** Determines if an axes permutation is the identity permutation. */
+export function isIdentityPermutation(perm) {
+    return perm.every((dim, index) => dim === index);
+}
+function findTermsWithDim(idDims, dim) {
+    const termIndices = [];
+    for (let i = 0; i < idDims.length; ++i) {
+        if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {
+            termIndices.push(i);
+        }
+    }
+    return termIndices;
+}
+//# sourceMappingURL=einsum_util.js.map
@@ -0,0 +1 @@
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert<T>(\n    arr: T[], element: T, comparator?: (a: T, b: T) => number) {\n  const index = binarySearch(arr, element, comparator);\n  const insertionPoint = index < 0 ? -(index + 1) : index;\n  arr.splice(insertionPoint, 0, element);\n}\n\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch<T>(\n    arr: T[], target: T, comparator?: (a: T, b: T) => number) {\n  return binarySearch_(arr, target, comparator || defaultComparator);\n}\n\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator<T>(a: T, b: T): number {\n  return a > b ? 1 : a < b ? -1 : 0;\n}\n\nfunction binarySearch_<T>(\n    arr: T[], target: T, comparator: (a: T, b: T) => number) {\n  let left = 0;\n  let right = arr.length;\n  let middle = 0;\n  let found = false;\n  while (left < right) {\n    middle = left + ((right - left) >>> 1);\n    const compareResult = comparator(target, arr[middle]);\n    if (compareResult > 0) {\n      left = middle + 1;\n    } else {\n      right = middle;\n      // If compareResult is 0, the value is found. 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See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport {version} from './version';\n\nexport {InferenceModel, MetaGraph, MetaGraphInfo, ModelPredictConfig, ModelTensorInfo, SavedModelTensorInfo, SignatureDef, SignatureDefEntry, SignatureDefInfo} from './model_types';\n// Optimizers.\nexport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nexport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nexport {AdamOptimizer} from './optimizers/adam_optimizer';\nexport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nexport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nexport {Optimizer} from './optimizers/optimizer';\nexport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nexport {SGDOptimizer} from './optimizers/sgd_optimizer';\nexport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, TensorBuffer, Variable} from './tensor';\nexport {GradSaveFunc, NamedTensorMap, TensorContainer, TensorContainerArray, TensorContainerObject} from './tensor_types';\nexport {BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, PixelData, Rank, RecursiveArray, ScalarLike, ShapeMap, sumOutType, TensorLike, TypedArray, upcastType} from './types';\n\nexport * from './ops/ops';\nexport {Reduction} from './ops/loss_ops_utils';\n\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport {customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads} from './gradients';\n\nexport {TimingInfo, MemoryInfo, ForwardFunc} from './engine';\nexport {EnvirLine truncated
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+{"version":3,"file":"browser_util_test.js","sourceRoot":"","sources":["../src/browser_util_test.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,KAAK,EAAE,MAAM,SAAS,CAAC;AAC9B,OAAO,EAAC,QAAQ,EAAE,iBAAiB,EAAC,MAAM,gBAAgB,CAAC;AAE3D,iBAAiB,CAAC,WAAW,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC5C,EAAE,CAAC,aAAa,EAAE,KAAK,IAAI,EAAE;QAC3B,MAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;QACzB,MAAM,EAAE,CAAC,SAAS,EAAE,CAAC;QACrB,MAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;QACzB,qEAAqE;QACrE,MAAM,CAAC,EAAE,GAAG,EAAE,CAAC,CAAC;IAClB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uBAAuB,EAAE,KAAK,IAAI,EAAE;QACrC,IAAI,IAAI,GAAG,KAAK,CAAC;QACjB,UAAU,CAAC,GAAG,EAAE;YACd,IAAI,GAAG,IAAI,CAAC;QACd,CAAC,EAAE,EAAE,CAAC,CAAC;QACP,MAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;QACzB,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QACzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,IAAI,IAAI,CAAC,IAAI,EAAE;YACzC,MAAM,EAAE,CAAC,SAAS,EAAE,CAAC;SACtB;QACD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC1B,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from './index';\nimport {ALL_ENVS, describeWithFlags} from './jasmine_util';\n\ndescribeWithFlags('nextFrame', ALL_ENVS, () => {\n  it('basic usage', async () => {\n    const t0 = tf.util.now();\n    await tf.nextFrame();\n    const t1 = tf.util.now();\n    // tf.util.now should give sufficient accuracy on all supported envs.\n    expect(t1 > t0);\n  });\n\n  it('does not block timers', async () => {\n    let flag = false;\n    setTimeout(() => {\n      flag = true;\n    }, 50);\n    const t0 = tf.util.now();\n    expect(flag).toBe(false);\n    while (tf.util.now() - t0 < 1000 && !flag) {\n      await tf.nextFrame();\n    }\n    expect(flag).toBe(true);\n  });\n});\n"]}
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under Line truncated
@@ -14,5 +14,5 @@
 * limitations under the License.
 * =============================================================================
 */
-export declare function isMobile(): boolean;
+export declare function isMobile(nav?: Navigator): boolean;
 export declare function isBrowser(): boolean;
@@ -18,10 +18,16 @@
 function _isNavigatorDefined() {
    return typeof navigator !== 'undefined' && navigator != null;
 }
-export function isMobile() {
-    if (_isNavigatorDefined()) {
+export function isMobile(nav) {
+    if (nav || _isNavigatorDefined()) {
+        if (!nav) {
+            nav = navigator;
+        }
+        if (nav.product === 'ReactNative') {
+            return true;
+        }
        // tslint:disable-next-line:no-any
-        const a = navigator.userAgent || navigator.vendor || window.opera;
+        const a = nav.userAgent || nav.vendor || window.opera;
        // tslint:disable-next-line:max-line-length
        return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i
            .test(a) ||
@@ -1 +1 @@
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+{"version":3,"file":"device_util.js","sourceRoot":"","sources":["../src/device_util.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,kCAAkC;AAClC,SAAS,mBAAmB;IAC1B,OAAO,OAAO,SAAS,KAAK,WAAW,IAAI,SAAS,IAAI,IAAI,CAAC;AAC/D,CAAC;AAED,MAAM,UAAU,QAAQ,CAAC,GAAe;IACtC,IAAI,GAAG,IAAI,mBAAmB,EAAE,EAAE;QAChC,IAAI,CAAC,GAAG,EAAE;YACR,GAAG,GAAG,SAAS,CAAC;SACjB;QACD,IAAI,GAAG,CAAC,OAAO,KAAK,aAAa,EAAE;YACjC,OAAO,IAAI,CAAC;SACb;QAED,kCAAkC;QAClC,MAAM,CAAC,GAAG,GAAG,CAAC,SAAS,IAAI,GAAG,CAAC,MAAM,IAAK,MAAc,CAAC,KAAK,CAAC;QAC/D,2CAA2C;QAC3C,OAAO,0TAA0T;aACrT,IAAI,CAAC,CAAC,CAAC;YACf,2CAA2C;YAC3C,ykDAAykD;iBACpkD,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;KAC/B;IACD,OAAO,KAAK,CAAC;AACf,CAAC;AAED,MAAM,UAAU,SAAS;IACvB,OAAO,CAAC,OAAO,MAAM,KAAK,WAAW,IAAI,MAAM,CAAC,QAAQ,IAAI,IAAI,CAAC;QAC7D,YAAY;QACZ,CAAC,OAAO,iBAAiB,KAAK,WAAW,CAAC,CAAC;AACjD,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined(): boolean {\n  return typeof navigator !== 'undefined' && navigator != null;\n}\n\nexport function isMobile(nav?: Navigator): boolean {\n  if (nav || _isNavigatorDefined()) {\n    if (!nav) {\n      nav = navigator;\n    }\n    if (nav.product === 'ReactNative') {\n      return true;\n    }\n\n    // tslint:disable-next-line:no-any\n    const a = nav.userAgent || nav.vendor || (window as any).opera;\n    // tslint:disable-next-line:max-line-length\n    return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n               .test(a) ||\n        // tslint:disable-next-line:max-line-length\n        /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n            .test(a.substr(0, 4));\n  }\n  return false;\n}\n\nexport function isBrowser(): boolean {\n  return (typeof window !== 'undefined' && window.document != null) ||\n      //@ts-ignore\n      (typeof WorkerGlobalScope !== 'undefined');\n}\n"]}
@@ -20,5 +20,8 @@ describeWithFlags('isMobile', ALL_ENVS, () => {
    it('should not fail when navigator is set', () => {
        expect(() => device_util.isMobile()).not.toThrow();
    });
+    it('identifies react native as a mobile device', () => {
+        expect(device_util.isMobile({ product: 'ReactNative' })).toEqual(true);
+    });
 });
 //# sourceMappingURL=device_util_test.js.map
@@ -1 +1 @@
-{"version":3,"file":"device_util_test.js","sourceRoot":"","sources":["../src/device_util_test.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,KAAK,WAAW,MAAM,eAAe,CAAC;AAC7C,OAAO,EAAC,QAAQ,EAAE,iBAAiB,EAAC,MAAM,gBAAgB,CAAC;AAE3D,iBAAiB,CAAC,UAAU,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC3C,EAAE,CAAC,uCAAuC,EAAE,GAAG,EAAE;QAC/C,MAAM,CAAC,GAAG,EAAE,CAAC,WAAW,CAAC,QAAQ,EAAE,CAAC,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC;IACrD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
+{"version":3,"file":"device_util_test.js","sourceRoot":"","sources":["../src/device_util_test.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,KAAK,WAAW,MAAM,eAAe,CAAC;AAC7C,OAAO,EAAC,QAAQ,EAAE,iBAAiB,EAAC,MAAM,gBAAgB,CAAC;AAE3D,iBAAiB,CAAC,UAAU,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC3C,EAAE,CAAC,uCAAuC,EAAE,GAAG,EAAE;QAC/C,MAAM,CAAC,GAAG,EAAE,CAAC,WAAW,CAAC,QAAQ,EAAE,CAAC,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC;IACrD,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,4CAA4C,EAAE,GAAG,EAAE;QACpD,MAAM,CAAC,WAAW,CAAC,QAAQ,CACzB,EAAC,OAAO,EAAE,aAAa,EAAc,CAAC,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC;IAC1D,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as device_util from './device_util';\nimport {ALL_ENVS, describeWithFlags} from './jasmine_util';\n\ndescribeWithFlags('isMobile', ALL_ENVS, () => {\n  it('should not fail when navigator is set', () => {\n    expect(() => device_util.isMobile()).not.toThrow();\n  });\n  it('identifies react native as a mobile device', () => {\n    expect(device_util.isMobile(\n      {product: 'ReactNative'} as Navigator)).toEqual(true);\n  });\n});\n"]}
@@ -99,7 +99,6 @@ declare class EngineState {
        bytes: number;
        dtype: "string" | "float32" | "int32" | "bool" | "complex64";
        shape: number[];
-        refCount: number;
    }>;
    profiling: boolean;
    activeProfile: ProfileInfo;
@@ -205,7 +204,9 @@ export declare class Engine implements TensorTracker, DataMover {
     */
    makeTensorFromDataId(dataId: DataId, shape: number[], dtype: DataType, backend?: KernelBackend): Tensor;
    makeVariable(initialValue: Tensor, trainable?: boolean, name?: string, dtype?: DataType): Variable;
+    trackTensor(a: Tensor, backend: KernelBackend): void;
    incRef(a: Tensor, backend: KernelBackend): void;
+    removeDataId(dataId: DataId, backend: KernelBackend): void;
    disposeTensor(a: Tensor): void;
    disposeVariables(): void;
    disposeVariable(v: Variable): void;
@@ -17,7 +17,7 @@
 import { KernelBackend } from './backends/backend';
 import { Environment, setEnvironmentGlobal } from './environment';
 import { getGlobalNamespace } from './global_util';
-import { Add, Cast } from './kernel_names';
+import { Add, Cast, Identity } from './kernel_names';
 import { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';
 import { Profiler } from './profiler';
 import { backpropagateGradients, getFilteredNodesXToY } from './tape';
@@ -287,11 +287,12 @@ export class Engine {
        const info = this.state.tensorInfo.get(dataId);
        const srcBackend = info.backend;
        const values = this.readSync(dataId);
+        const refCount = srcBackend.refCount(dataId);
        // Delete the tensor from the old backend and move it to the new
        // backend.
-        srcBackend.disposeData(dataId);
+        srcBackend.disposeData(dataId, true);
        info.backend = backend;
-        backend.move(dataId, values, info.shape, info.dtype);
+        backend.move(dataId, values, info.shape, info.dtype, refCount);
        if (this.shouldCheckForMemLeaks()) {
            // Track the number of moves during a kernel execution to correctly
            // detect memory leaks.
@@ -355,7 +356,7 @@ export class Engine {
     * execution.
     */
    clone(x) {
-        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);
+        const y = ENGINE.runKernel(Identity, { x });
        const inputs = { x };
        const grad = (dy) => ({
            x: () => {
@@ -607,7 +608,7 @@ export class Engine {
        }
        const dataId = backend.write(backendVals, shape, dtype);
        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-        this.incRef(t, backend);
+        this.trackTensor(t, backend);
        // Count bytes for string tensors.
        if (dtype === 'string') {
            const info = this.state.tensorInfo.get(dataId);
@@ -625,7 +626,7 @@ export class Engine {
    makeTensorFromDataId(dataId, shape, dtype, backend) {
        dtype = dtype || 'float32';
        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
-        this.incRef(t, backend);
+        this.trackTensor(t, backend);
        return t;
    }
    makeVariable(initialValue, trainable = true, name, dtype) {
@@ -641,63 +642,66 @@ export class Engine {
        this.incRef(v, this.backend);
        return v;
    }
-    incRef(a, backend) {
-        const refCount = this.state.tensorInfo.has(a.dataId) ?
-            this.state.tensorInfo.get(a.dataId).refCount :
-            0;
+    trackTensor(a, backend) {
        this.state.numTensors++;
        if (a.dtype === 'string') {
            this.state.numStringTensors++;
        }
-        if (refCount === 0) {
+        // Bytes for complex numbers are counted by their components. Bytes for
+        // string tensors are counted when writing values.
+        let bytes = 0;
+        if (a.dtype !== 'complex64' && a.dtype !== 'string') {
+            bytes = a.size * util.bytesPerElement(a.dtype);
+        }
+        this.state.numBytes += bytes;
+        if (!this.state.tensorInfo.has(a.dataId)) {
            this.state.numDataBuffers++;
-            // Bytes for complex numbers are counted by their components. Bytes for
-            // string tensors are counted when writing values.
-            let bytes = 0;
-            if (a.dtype !== 'complex64' && a.dtype !== 'string') {
-                bytes = a.size * util.bytesPerElement(a.dtype);
-            }
            this.state.tensorInfo.set(a.dataId, {
                backend: backend || this.backend,
                dtype: a.dtype,
                shape: a.shape,
-                bytes,
-                refCount: 0
+                bytes
            });
-            this.state.numBytes += bytes;
        }
-        this.state.tensorInfo.get(a.dataId).refCount++;
        if (!(a instanceof Variable)) {
            this.track(a);
        }
    }
+    // Track the tensor by dataId and increase the refCount for the dataId in the
+    // backend.
+    // TODO(pyu10055): This is currently used by makeVariable method, to increase
+    // refCount on the backend for the dataId. It can potentially be replaced with
+    // Identity op indead of calling backend directly.
+    incRef(a, backend) {
+        this.trackTensor(a, backend);
+        this.backend.incRef(a.dataId);
+    }
+    removeDataId(dataId, backend) {
+        if (this.state.tensorInfo.has(dataId) &&
+            this.state.tensorInfo.get(dataId).backend === backend) {
+            this.state.tensorInfo.delete(dataId);
+            this.state.numDataBuffers--;
+        }
+    }
    disposeTensor(a) {
        if (!this.state.tensorInfo.has(a.dataId)) {
            return;
        }
+        const info = this.state.tensorInfo.get(a.dataId);
        this.state.numTensors--;
        if (a.dtype === 'string') {
            this.state.numStringTensors--;
+            this.state.numBytes -= info.bytes;
        }
-        const info = this.state.tensorInfo.get(a.dataId);
-        const refCount = info.refCount;
-        if (refCount <= 1) {
-            // Don't count bytes for complex numbers as they are counted by their
-            // components.
-            if (a.dtype !== 'complex64') {
-                this.state.numBytes -= info.bytes;
-            }
-            this.state.numDataBuffers--;
-            info.backend.disposeData(a.dataId);
-            this.state.tensorInfo.delete(a.dataId);
+        // Don't count bytes for complex numbers as they are counted by their
+        // components.
+        if (a.dtype !== 'complex64' && a.dtype !== 'string') {
+            const bytes = a.size * util.bytesPerElement(a.dtype);
+            this.state.numBytes -= bytes;
        }
-        else {
-            // Notify the backend to descrease the ref count for complex tensor
-            // components. This method is only implemented in WebGL right now. When
-            // there are multiple references, complex tensor cannot dispose the
-            // components if ref count is not in sync with engine.
-            info.backend.decComplexRef(a.dataId);
-            this.state.tensorInfo.get(a.dataId).refCount--;
+        // Remove the reference to dataId if backend dispose the data successfully
+        if (info.backend.disposeData(a.dataId)) {
+            this.removeDataId(a.dataId, info.backend);
        }
        // TODO(nsthorat): Construct an error and save the stack trace for
        // debugging when in debug mode. Creating a stack trace is too expensive
@@ -1 +1 @@
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@@ -24,15 +24,15 @@ describe('Backend registration', () => {
        spyOn(console, 'warn');
    });
    let registeredBackends = [];
+    let registerBackend;
    beforeEach(() => {
        // Registering a backend changes global state (engine), so we wrap
        // registration to automatically remove registered backend at the end
        // of each test.
-        spyOn(tf, 'registerBackend')
-            .and.callFake((name, factory, priority) => {
+        registerBackend = (name, factory, priority) => {
            registeredBackends.push(name);
-            return ENGINE.registerBackend(name, factory, priority);
-        });
+            return tf.registerBackend(name, factory, priority);
+        };
        ENGINE.reset();
    });
    afterEach(() => {
@@ -54,7 +54,7 @@ describe('Backend registration', () => {
            }
            return newBackend;
        };
-        tf.registerBackend('test-backend', factory);
+        registerBackend('test-backend', factory);
        expect(tf.findBackend('test-backend') != null).toBe(true);
        expect(tf.findBackend('test-backend')).toBe(backend);
        expect(tf.findBackendFactory('test-backend')).toBe(factory);
@@ -73,7 +73,7 @@ describe('Backend registration', () => {
            }
            return newBackend;
        };
-        tf.registerBackend('custom-cpu', factory);
+        registerBackend('custom-cpu', factory);
        expect(tf.findBackend('custom-cpu') != null).toBe(true);
        expect(tf.findBackend('custom-cpu')).toBe(backend);
        expect(tf.findBackendFactory('custom-cpu')).toBe(factory);
@@ -81,7 +81,7 @@ describe('Backend registration', () => {
    it('custom backend registration', () => {
        let backend;
        const priority = 103;
-        tf.registerBackend('custom-cpu', () => {
+        registerBackend('custom-cpu', () => {
            const newBackend = new TestKernelBackend();
            if (backend == null) {
                backend = newBackend;
@@ -95,11 +95,11 @@ describe('Backend registration', () => {
        let lowPriorityBackend;
        const lowPriority = 103;
        const highPriority = 104;
-        tf.registerBackend('custom-low-priority', () => {
+        registerBackend('custom-low-priority', () => {
            lowPriorityBackend = new TestKernelBackend();
            return lowPriorityBackend;
        }, lowPriority);
-        tf.registerBackend('custom-high-priority', () => {
+        registerBackend('custom-high-priority', () => {
            throw new Error(`High priority backend fails`);
        }, highPriority);
        expect(tf.backend() != null).toBe(true);
@@ -111,11 +111,11 @@ describe('Backend registration', () => {
        let highPriorityBackend;
        const lowPriority = 103;
        const highPriority = 104;
-        tf.registerBackend('custom-low-priority', () => {
+        registerBackend('custom-low-priority', () => {
            lowPriorityBackend = new TestKernelBackend();
            return lowPriorityBackend;
        }, lowPriority);
-        tf.registerBackend('custom-high-priority', () => {
+        registerBackend('custom-high-priority', () => {
            highPriorityBackend = new TestKernelBackend();
            return highPriorityBackend;
        }, highPriority);
@@ -132,13 +132,13 @@ describe('Backend registration', () => {
    });
    it('allow custom backend', () => {
        const backend = new TestKernelBackend();
-        const success = tf.registerBackend('custom', () => backend);
+        const success = registerBackend('custom', () => backend);
        expect(success).toBeTruthy();
        expect(tf.findBackend('custom')).toEqual(backend);
    });
    it('sync backend with await ready works', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('sync', () => testBackend);
+        registerBackend('sync', () => testBackend);
        tf.setBackend('sync');
        expect(tf.getBackend()).toEqual('sync');
        await tf.ready();
@@ -146,14 +146,14 @@ describe('Backend registration', () => {
    });
    it('sync backend without await ready works', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('sync', () => testBackend);
+        registerBackend('sync', () => testBackend);
        tf.setBackend('sync');
        expect(tf.getBackend()).toEqual('sync');
        expect(tf.backend()).toEqual(testBackend);
    });
    it('async backend with await ready works', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return testBackend;
        });
@@ -164,7 +164,7 @@ describe('Backend registration', () => {
    });
    it('async backend without await ready does not work', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return testBackend;
        });
@@ -174,7 +174,7 @@ describe('Backend registration', () => {
            .toThrowError(/Backend 'async' has not yet been initialized./);
    });
    it('tf.square() fails if user does not await ready on async backend', async () => {
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return new TestKernelBackend();
        });
@@ -183,7 +183,7 @@ describe('Backend registration', () => {
            .toThrowError(/Backend 'async' has not yet been initialized/);
    });
    it('tf.square() works when user awaits ready on async backend', async () => {
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return new TestKernelBackend();
        });
@@ -193,11 +193,11 @@ describe('Backend registration', () => {
    });
    it('Registering async2 (higher priority) fails, async1 becomes active', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('async1', async () => {
+        registerBackend('async1', async () => {
            await tf.nextFrame();
            return testBackend;
        }, 100 /* priority */);
-        tf.registerBackend('async2', async () => {
+        registerBackend('async2', async () => {
            await tf.nextFrame();
            throw new Error('failed to create async2');
        }, 101 /* priority */);
@@ -209,8 +209,8 @@ describe('Backend registration', () => {
    });
    it('Registering sync as higher priority and async as lower priority', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('sync', () => testBackend, 101 /* priority */);
-        tf.registerBackend('async', async () => {
+        registerBackend('sync', () => testBackend, 101 /* priority */);
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return new TestKernelBackend();
        }, 100 /* priority */);
@@ -220,27 +220,27 @@ describe('Backend registration', () => {
    });
    it('async as higher priority and sync as lower priority with await ready', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return testBackend;
        }, 101 /* priority */);
-        tf.registerBackend('sync', () => new TestKernelBackend(), 100 /* priority */);
+        registerBackend('sync', () => new TestKernelBackend(), 100 /* priority */);
        await tf.ready();
        expect(tf.backend()).toEqual(testBackend);
        expect(tf.getBackend()).toBe('async');
    });
    it('async as higher priority and sync as lower priority w/o await ready', async () => {
        const testBackend = new TestKernelBackend();
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            return testBackend;
        }, 101 /* priority */);
-        tf.registerBackend('sync', () => new TestKernelBackend(), 100 /* priority */);
+        registerBackend('sync', () => new TestKernelBackend(), 100 /* priority */);
        expect(() => tf.backend())
            .toThrowError(/The highest priority backend 'async' has not yet been/);
    });
    it('Registering and setting a backend that fails to register', async () => {
-        tf.registerBackend('async', async () => {
+        registerBackend('async', async () => {
            await tf.nextFrame();
            throw new Error('failed to create async');
        });
@@ -592,8 +592,7 @@ describeWithFlags('Detects memory leaks in kernels', ALL_ENVS, () => {
                id: 1,
                dispose: () => null,
                disposeData: (dataId) => null,
-                numDataIds: () => dataIdsCount,
-                decComplexRef: (dataId) => null
+                numDataIds: () => dataIdsCount
            };
        });
        tf.setBackend(backendName);
@@ -1 +1 @@
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truncated
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Line truncated
@@ -38,6 +38,7 @@ export declare class Environment {
    private urlFlags;
    platformName: string;
    platform: Platform;
+    getQueryParams: typeof getQueryParams;
    constructor(global: any);
    setPlatform(platformName: string, platform: Platform): void;
    registerFlag(flagName: string, evaluationFn: FlagEvaluationFn, setHook?: (value: FlagValue) => void): void;
@@ -31,6 +31,8 @@ export class Environment {
        this.flags = {};
        this.flagRegistry = {};
        this.urlFlags = {};
+        // Jasmine spies on this in 'environment_test.ts'
+        this.getQueryParams = getQueryParams;
        this.populateURLFlags();
    }
    setPlatform(platformName, platform) {
@@ -112,7 +114,7 @@ export class Environment {
            typeof this.global.location.search === 'undefined') {
            return;
        }
-        const urlParams = getQueryParams(this.global.location.search);
+        const urlParams = this.getQueryParams(this.global.location.search);
        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
            keyValues.forEach(keyValue => {
@@ -1 +1 @@
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitatLine truncated
@@ -20,18 +20,18 @@ describe('initializes flags from the url', () => {
    // Silence console.warns for these tests.
    beforeAll(() => spyOn(console, 'warn').and.returnValue(null));
    it('no overrides one registered flag', () => {
-        spyOn(environment, 'getQueryParams').and.returnValue({});
        const global = { location: { search: '' } };
        const env = new Environment(global);
+        spyOn(env, 'getQueryParams').and.returnValue({});
        env.registerFlag('FLAG1', () => false);
        expect(env.get('FLAG1')).toBe(false);
    });
    it('one unregistered flag', () => {
-        spyOn(environment, 'getQueryParams').and.returnValue({
-            'tfjsflags': 'FLAG1:true'
-        });
        const global = { location: { search: '' } };
        const env = new Environment(global);
+        spyOn(env, 'getQueryParams').and.returnValue({
+            'tfjsflags': 'FLAG1:true'
+        });
        expect(env.features).toEqual({});
    });
    it('one registered flag true', () => {
@@ -1 +1 @@
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@@ -55,4 +55,6 @@ ENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);
 ENV.registerFlag('IS_TEST', () => false);
 /** Whether to check computation result for errors. */
 ENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', () => true);
+/** Whether the backend needs to wrap input to imageBitmap. */
+ENV.registerFlag('WRAP_TO_IMAGEBITMAP', () => false);
 //# sourceMappingURL=flags.js.map
@@ -1 +1 @@
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@@ -1 +1 @@
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+{"version":3,"file":"globals.js","sourceRoot":"","sources":["../src/globals.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAGH,OAAO,EAAC,MAAM,EAAuD,MAAM,UAAU,CAAC;AACtF,OAAO,EAAC,GAAG,EAAC,MAAM,eAAe,CAAC;AAGlC,OAAO,EAAC,uBAAuB,EAAS,MAAM,UAAU,CAAC;AAEzD,OAAO,EAAC,qBAAqB,EAAC,MAAM,eAAe,CAAC;AAEpD;;;;;GAKG;AACH,MAAM,UAAU,cAAc;IAC5B,GAAG,EAAE,CAAC,GAAG,CAAC,MAAM,EAAE,IAAI,CAAC,CAAC;AAC1B,CAAC;AAED;;;;;;;;;;;;;GAaG;AACH,MAAM,UAAU,eAAe;IAC7B,GAAG,EAAE,CAAC,GAAG,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;AAC3B,CAAC;AAED,6CAA6C;AAC7C,MAAM,UAAU,0BAA0B;IACxC,GAAG,EAAE,CAAC,GAAG,CAAC,8BAA8B,EAAE,KAAK,CAAC,CAAC;IACjD,OAAO,CAAC,IAAI,CAAC,wDAAwD,CAAC,CAAC;AACzE,CAAC;AAED,iDAAiD;AACjD,MAAM,UAAU,eAAe,CAAC,GAAW;IACzC,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,8BAA8B,CAAC,EAAE;QACjD,OAAO,CAAC,IAAI,CACR,GAAG,GAAG,6CAA6C;YACnD,kCAAkC,CAAC,CAAC;KACzC;AACH,CAAC;AACD,uBAAuB,CAAC,eAAe,CAAC,CAAC;AAEzC;;;;GAIG;AACH,MAAM,UAAU,gBAAgB;IAC9B,MAAM,CAAC,gBAAgB,EAAE,CAAC;AAC5B,CAAC;AAED;;;;GAIG;AACH,MAAM,UAAU,MAAM;IACpB,OAAO,MAAM,CAAC;AAChB,CAAC;AAED;;;;;;;;;;;;;;;;;;;;GAoBG;AACH,MAAM,UAAU,MAAM;IACpB,OAAO,MAAM,CAAC,MAAM,EAAE,CAAC;AACzB,CAAC;AAED;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA8BG;AACH,MAAM,UAAU,OAAO,CAAC,CAAqD;IAE3E,OAAO,MAAM,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;AAC3B,CAAC;AAED;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAuCG;AACH,MAAM,UAAU,IAAI,CAChB,QAA2B,EAAE,EAAe;IAC9C,OAAO,MAAM,CAAC,IAAI,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;AACnC,CAAC;AAED;;;;;;;;;;GAUG;AACH,MAAM,UAAU,OAAO,CAAC,SAA0B;IAChD,MAAM,OAAO,GAAG,qBAAqB,CAAC,SAAS,CAAC,CAAC;IACjD,OAAO,CAAC,OAAO,CAAC,MAAM,CAAC,EAAE,CAAC,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC;AAC9C,CAAC;AAED;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA+BG;AACH,MAAM,UAAU,IAAI,CAAmB,MAAS;IAC9C,OAAO,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;AAC7B,CAAC;AAED;;;;;;;;;;;;;;;;;;;;;;;;GAwBG;AACH,MAAM,UAAU,IAAI,CAAC,CAAa;IAChC,OAAO,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;AACxB,CAAC;AAED;;;;;;;;;;;;;;GAcG;AACH,MAAM,UAAU,UAAU,CAAC,WAAmB;IAC5C,OAAO,MAAM,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC;AACxC,CAAC;AAED;;;;;;GAMG;AACH,MAAM,UAAU,KAAK;IACnB,OAAO,MAAM,CAAC,KAAK,EAAE,CAAC;AACxB,CAAC;AAED;;;;;GAKG;AACH,MAAM,UAAU,UAAU;IACxB,OAAO,MAAM,CAAC,WAAW,CAAC;AAC5B,CAAC;AAED;;;;GAIG;AACH,MAAM,UAAU,aAAa,CAAC,IAAY;IACxC,MAAM,CAAC,aAAa,CAAC,IAAI,CAAC,CAAC;AAC7B,CAAC;AAED;;;GAGG;AACH,MAAM,UAAU,WAAW,CAAC,IAAY;IACtC,OAAO,MAAM,CAAC,WAAW,CAAC,IAAI,CAAC,CAAC;AAClC,CAAC;AAED;;;;GAIG;AACH,MAAM,UAAU,kBAAkB,CAAC,IAAY;IAE7C,OAAO,MAAM,CAAC,kBAAkB,CAAC,IAAI,CAAC,CAAC;AACzC,CAAC;AAED;;;;;;;;;;;;;;GAcG;AACH,MAAM,UAAU,eAAe,CAC3B,IAAY,EAAE,OAAqD,EACnE,QAAQ,GAAG,CAAC;IACd,OAAO,MAAM,CAAC,eAAe,CAAC,IAAI,EAAE,OAAO,EAAE,QAAQ,CAAC,CAAC;AACzD,CAAC;AAED;;;;;;;GAOG;AACH,MAAM,UAAU,OAAO;IACrB,OAAO,MAAM,CAAC,OAAO,CAAC;AACxB,CAAC;AAED;;;;;GAKG;AACH,MAAM,UAAU,WAAW,CAAC,YAAoB,EAAE,QAAkB;IAClE,GAAG,EAAE,CAAC,WAAW,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;AAC5C,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from './backends/backend';\nimport {ENGINE, Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {env} from './environment';\n\nimport {Platform} from './platforms/platform';\nimport {setDeprecationWarningFn, Tensor} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode(): void {\n  env().set('PROD', true);\n}\n\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode(): void {\n  env().set('DEBUG', true);\n}\n\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings(): void {\n  env().set('DEPRECATION_WARNINGS_ENABLED', false);\n  console.warn(`TensorFlow.js deprecation warnings have been disabLine truncated
@@ -22,7 +22,11 @@ describe('deprecation warnings', () => {
        spyOn(console, 'warn').and.callFake((msg) => null);
    });
    it('deprecationWarn warns', () => {
+        // flags_test.ts verifies deprecation warnings are on by default.
+        const deprecationVal = tf.env().get('DEPRECATION_WARNINGS_ENABLED');
+        tf.env().set('DEPRECATION_WARNINGS_ENABLED', true);
        tf.deprecationWarn('xyz is deprecated.');
+        tf.env().set('DEPRECATION_WARNINGS_ENABLED', deprecationVal);
        expect(console.warn).toHaveBeenCalledTimes(1);
        expect(console.warn)
            .toHaveBeenCalledWith('xyz is deprecated. You can disable deprecation warnings with ' +
@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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+{"version":3,"file":"Cosh_grad.js","sourceRoot":"","sources":["../../src/gradients/Cosh_grad.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,EAAC,IAAI,EAAC,MAAM,iBAAiB,CAAC;AAErC,OAAO,EAAC,IAAI,EAAC,MAAM,aAAa,CAAC;AACjC,OAAO,EAAC,GAAG,EAAC,MAAM,YAAY,CAAC;AAC/B,OAAO,EAAC,IAAI,EAAC,MAAM,aAAa,CAAC;AAGjC,MAAM,CAAC,MAAM,cAAc,GAAe;IACxC,UAAU,EAAE,IAAI;IAChB,YAAY,EAAE,CAAC,GAAG,CAAC;IACnB,QAAQ,EAAE,CAAC,EAAU,EAAE,KAAe,EAAE,EAAE;QACxC,MAAM,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC;QAElB,OAAO,EAAC,CAAC,EAAE,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,EAAE,EAAE,CAAC,EAAC,CAAC;IACtD,CAAC;CACF,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cosh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {sinh} from '../ops/sinh';\nimport {Tensor} from '../tensor';\n\nexport const coshGradConfig: GradConfig = {\n  kernelName: Cosh,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => mul(sinh(cast(x, 'float32')), dy)};\n  }\n};\n"]}
@@ -1 +1 @@
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport * as conv_util from '../ops/conv_util';\nimport {depthwiseConv2dNativeBackpropFilter} from '../ops/depthwise_conv2d_native_backprop_filter';\nimport {depthwiseConv2dNativeBackpropInput} from '../ops/depthwise_conv2d_native_backprop_input';\nimport {Tensor, Tensor4D} from '../tensor';\nimport * as util from '../util';\n\nexport const depthwiseConv2dNativeGradConfig: GradConfig = {\n  kernelName: DepthwiseConv2dNative,\n  inputsToSave: ['x', 'filter'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {dilations, strides, pad, dimRoundingMode} =\n        attrs as {} as DepthwiseConv2dNativeAttrs;\n\n    const $dilations = dilations == null ? [1, 1] : dilations;\n\n    util.assert(\n        conv_util.tupleValuesAreOne($dilations),\n        () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n\n    const [x, filter] = saved as [Tensor4D, Tensor4D];\n\n    util.assert(\n        x.rank === 4,\n        () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n    util.assert(\n        filter.rank === 4,\n        () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n    util.assert(\n        x.shape[3] === filter.shape[2],\n        () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n\n    util.assert(\n        conv_util.eitherStridesOrDilationsAreOne(strides, $dilations),\n        () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n\n    if (dimRoundingMode != null) {\n      util.assert(\n          util.isInt(pad as number),\n          () =>\n              `Error in depthwiseConv2d: pad must be an integer when using, ` +\n              `dimRoundingMode ${dimRounLine truncated
@@ -1 +1 @@
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+{"version":3,"file":"Dilation2D_grad.js","sourceRoot":"","sources":["../../src/gradients/Dilation2D_grad.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AACH,OAAO,EAAC,MAAM,EAAC,MAAM,WAAW,CAAC;AACjC,OAAO,EAAC,UAAU,EAAE,wBAAwB,EAAkC,uBAAuB,EAAgC,MAAM,iBAAiB,CAAC;AAM7J,MAAM,CAAC,MAAM,oBAAoB,GAAe;IAC9C,UAAU,EAAE,UAAU;IACtB,YAAY,EAAE,CAAC,GAAG,EAAE,QAAQ,CAAC;IAC7B,QAAQ,EAAE,CAAC,EAAY,EAAE,KAAe,EAAE,KAAmB,EAAE,EAAE;QAC/D,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,KAA6B,CAAC;QAElD,MAAM,WAAW,GAAkC,EAAC,CAAC,EAAE,MAAM,EAAE,EAAE,EAAC,CAAC;QACnE,MAAM,YAAY,GAAmC,EAAC,CAAC,EAAE,MAAM,EAAE,EAAE,EAAC,CAAC;QAErE,OAAO;YACL,CAAC,EAAE,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,CACrB,uBAAuB,EAAE,WAAmC,EAAE,KAAK,CAAC;YACxE,MAAM,EAAE,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,CAC1B,wBAAwB,EAAE,YAAoC,EAAE,KAAK,CAAC;SAC3E,CAAC;IACJ,CAAC;CACF,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropFilterInputs, Dilation2DBackpropInput, Dilation2DBackpropInputInputs} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nexport const dilation2dGradConfig: GradConfig = {\n  kernelName: Dilation2D,\n  inputsToSave: ['x', 'filter'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x, filter] = saved as [Tensor4D, Tensor3D];\n\n    const inputInputs: Dilation2DBackpropInputInputs = {x, filter, dy};\n    const filterInputs: Dilation2DBackpropFilterInputs = {x, filter, dy};\n\n    return {\n      x: () => ENGINE.runKernel(\n          Dilation2DBackpropInput, inputInputs as {} as NamedTensorMap, attrs),\n      filter: () => ENGINE.runKernel(\n          Dilation2DBackpropFilter, filterInputs as {} as NamedTensorMap, attrs)\n    };\n  }\n};\n"]}
				`@@ -0,0 +1 @@`
				{"version":3,"file":"sparse.js","sourceRoot":"","sources":["../../../src/operations/op_list/sparse.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAIH,MAAM,CAAC,MAAM,IAAI,GAAe,CAAC;QAC/B,UAAU,EAAE,eAAe;QAC3B,UAAU,EAAE,QAAQ;QACpB,QAAQ,EAAE;YACR,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,cAAc,EAAE,MAAM,EAAE,QAAQ,EAAC;YACtD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,YAAY,EAAE,MAAM,EAAE,QAAQ,EAAC;YACpD,EAAC,OAAO,EAAE,CAAC,EAAE,MAAM,EAAE,UAAU,EAAE,MAAM,EAAE,QAAQ,EAAC;SACnD;QACD,OAAO,EACH,CAAC,EAAC,QAAQ,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,EAAE,MAAM,EAAE,OAAO,EAAE,cAAc,EAAE,IAAI,EAAC,CAAC;KAC9E,CAAC,CAAC","sourcesContent":["/*\n @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n \n http://www.apache.org/licenses/LICENSE-2.0\n \n Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OpMapper} from '../types';\n\nexport const json: OpMapper[] = [{\n 'tfOpName': 'SparseReshape',\n 'category': 'sparse',\n 'inputs': [\n {'start': 0, 'name': 'inputIndices', 'type': 'tensor'},\n {'start': 1, 'name': 'inputShape', 'type': 'tensor'},\n {'start': 2, 'name': 'newShape', 'type': 'tensor'},\n ],\n 'attrs':\n [{'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true}]\n}];\n"]}
				`@@ -0,0 +1 @@`
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