"use strict";
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (g && (g = 0, op[0] && (_ = 0)), _) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs");
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// tslint:disable-next-line: no-imports-from-dist
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var jasmine_util_1 = require("@tensorflow/tfjs-core/dist/jasmine_util");
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var nodejs_kernel_backend_1 = require("./nodejs_kernel_backend");
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describe('delayed upload', function () {
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it('should handle data before op execution', function () { return __awaiter(void 0, void 0, void 0, function () {
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var t, _a, _b, r, _c, _d;
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return __generator(this, function (_e) {
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switch (_e.label) {
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case 0:
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t = tf.tensor1d([1, 2, 3]);
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_b = (_a = tf.test_util).expectArraysClose;
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return [4 /*yield*/, t.data()];
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case 1:
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_b.apply(_a, [_e.sent(), [1, 2, 3]]);
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r = t.add(tf.tensor1d([4, 5, 6]));
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_d = (_c = tf.test_util).expectArraysClose;
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return [4 /*yield*/, r.data()];
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case 2:
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_d.apply(_c, [_e.sent(), [5, 7, 9]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('Should not cache tensors in the tensor map for device support. ', function () {
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var logits = tf.tensor1d([1, 2, 3]);
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var softmaxLogits = tf.softmax(logits);
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var data = softmaxLogits.dataSync();
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expect(softmaxLogits.dataSync()[0]).toEqual(data[0]);
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expect(softmaxLogits.dataSync()[1]).toEqual(data[1]);
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expect(softmaxLogits.dataSync()[2]).toEqual(data[2]);
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});
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});
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describe('type casting', function () {
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it('exp support int32', function () {
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tf.exp(tf.scalar(2, 'int32'));
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});
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});
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describe('conv3d dilations', function () {
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it('CPU should throw error on dilations >1', function () {
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var input = tf.ones([1, 2, 2, 2, 1]);
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var filter = tf.ones([1, 1, 1, 1, 1]);
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expect(function () {
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tf.conv3d(input, filter, 1, 'same', 'NDHWC', [2, 2, 2]);
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}).toThrowError();
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});
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it('GPU should handle dilations >1', function () {
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// This test can only run locally with CUDA bindings and GPU package
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// installed.
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if (tf.backend().isUsingGpuDevice) {
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var input = tf.ones([1, 2, 2, 2, 1]);
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var filter = tf.ones([1, 1, 1, 1, 1]);
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tf.conv3d(input, filter, 1, 'same', 'NDHWC', [2, 2, 2]);
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}
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});
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});
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describe('Exposes Backend for internal Op execution.', function () {
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it('Provides the Node backend over a function', function () {
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var backend = (0, nodejs_kernel_backend_1.nodeBackend)();
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expect(backend instanceof nodejs_kernel_backend_1.NodeJSKernelBackend).toBeTruthy();
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});
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it('Provides internal access to the binding', function () {
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expect((0, nodejs_kernel_backend_1.nodeBackend)().binding).toBeDefined();
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});
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it('throw error if backend is not tensorflow', function () { return __awaiter(void 0, void 0, void 0, function () {
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var testBackend;
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return __generator(this, function (_a) {
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testBackend = new jasmine_util_1.TestKernelBackend();
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tf.registerBackend('fake', function () { return testBackend; });
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tf.setBackend('fake');
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try {
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expect(function () { return (0, nodejs_kernel_backend_1.ensureTensorflowBackend)(); }).toThrowError('Expect the current backend to be "tensorflow", but got "fake"');
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}
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finally {
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tf.setBackend('tensorflow');
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}
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return [2 /*return*/];
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});
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}); });
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});
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describe('getTFDType()', function () {
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var binding = (0, nodejs_kernel_backend_1.nodeBackend)().binding;
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it('handles float32', function () {
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expect((0, nodejs_kernel_backend_1.getTFDType)('float32')).toBe(binding.TF_FLOAT);
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});
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it('handles int32', function () {
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expect((0, nodejs_kernel_backend_1.getTFDType)('int32')).toBe(binding.TF_INT32);
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});
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it('handles bool', function () {
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expect((0, nodejs_kernel_backend_1.getTFDType)('bool')).toBe(binding.TF_BOOL);
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});
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it('handles unknown types', function () {
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expect(function () { return (0, nodejs_kernel_backend_1.getTFDType)(null); }).toThrowError();
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});
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});
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describe('createTypeOpAttr()', function () {
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var binding = (0, nodejs_kernel_backend_1.nodeBackend)().binding;
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it('Creates a valid type attribute', function () {
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var attr = (0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('foo', 'float32');
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expect(attr.name).toBe('foo');
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expect(attr.type).toBe(binding.TF_ATTR_TYPE);
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expect(attr.value).toBe(binding.TF_FLOAT);
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});
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it('handles unknown dtypes', function () {
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expect(function () { return (0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('foo', null); }).toThrowError();
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});
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});
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describe('Returns TFEOpAttr for a Tensor or list of Tensors', function () {
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var binding = (0, nodejs_kernel_backend_1.nodeBackend)().binding;
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it('handles a single Tensor', function () {
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var result = (0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', tf.scalar(13, 'float32'));
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expect(result.name).toBe('T');
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expect(result.type).toBe(binding.TF_ATTR_TYPE);
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expect(result.value).toBe(binding.TF_FLOAT);
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});
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it('handles a list of Tensors', function () {
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var tensors = [tf.scalar(1, 'int32'), tf.scalar(20.1, 'float32')];
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var result = (0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', tensors);
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expect(result.name).toBe('T');
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expect(result.type).toBe(binding.TF_ATTR_TYPE);
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expect(result.value).toBe(binding.TF_INT32);
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});
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it('handles null', function () {
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expect(function () { return (0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', null); }).toThrowError();
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});
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it('handles list of null', function () {
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var inputs = [null, null];
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expect(function () { return (0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', inputs); }).toThrowError();
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});
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});
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