@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
648 lines • 30.1 kB
JavaScript
"use strict";
/**
* @license
* Copyright 2019 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
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;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
jasmine_util_1.describeWithFlags('hannWindow', jasmine_util_1.ALL_ENVS, function () {
it('length=3', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hannWindow(3);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 1, 0]]);
return [2 /*return*/];
}
});
}); });
it('length=7', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hannWindow(7);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0, 0.25, 0.75, 1, 0.75, 0.25, 0]]);
return [2 /*return*/];
}
});
}); });
it('length=6', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hannWindow(6);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0., 0.25, 0.75, 1., 0.75, 0.25]]);
return [2 /*return*/];
}
});
}); });
it('length=20', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hannWindow(20);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0., 0.02447176, 0.09549153, 0.20610738, 0.34549153,
0.5, 0.65450853, 0.79389274, 0.9045085, 0.97552824,
1., 0.97552824, 0.9045085, 0.7938925, 0.65450835,
0.5, 0.34549144, 0.20610726, 0.09549153, 0.02447173
]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('hammingWindow', jasmine_util_1.ALL_ENVS, function () {
it('length=3', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hammingWindow(3);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.08, 1, 0.08]]);
return [2 /*return*/];
}
});
}); });
it('length=6', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hammingWindow(6);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.08, 0.31, 0.77, 1., 0.77, 0.31]]);
return [2 /*return*/];
}
});
}); });
it('length=7', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hammingWindow(7);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [0.08, 0.31, 0.77, 1, 0.77, 0.31, 0.08]]);
return [2 /*return*/];
}
});
}); });
it('length=20', function () { return __awaiter(_this, void 0, void 0, function () {
var ret, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
ret = tf.signal.hammingWindow(20);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, ret.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.08000001, 0.10251403, 0.16785222, 0.2696188, 0.3978522,
0.54, 0.68214786, 0.8103813, 0.9121479, 0.977486,
1., 0.977486, 0.9121478, 0.8103812, 0.6821477,
0.54, 0.39785212, 0.2696187, 0.16785222, 0.102514
]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('frame', jasmine_util_1.ALL_ENVS, function () {
it('3 length frames', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 3;
frameStep = 1;
output = tf.signal.frame(input, frameLength, frameStep);
expect(output.shape).toEqual([3, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 2, 3, 4, 3, 4, 5]]);
return [2 /*return*/];
}
});
}); });
it('3 length frames with step 2', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 3;
frameStep = 2;
output = tf.signal.frame(input, frameLength, frameStep);
expect(output.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 3, 4, 5]]);
return [2 /*return*/];
}
});
}); });
it('3 length frames with step 5', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 3;
frameStep = 5;
output = tf.signal.frame(input, frameLength, frameStep);
expect(output.shape).toEqual([1, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3]]);
return [2 /*return*/];
}
});
}); });
it('Exceeding frame length', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 6;
frameStep = 1;
output = tf.signal.frame(input, frameLength, frameStep);
expect(output.shape).toEqual([0, 6]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), []]);
return [2 /*return*/];
}
});
}); });
it('Zero frame step', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 6;
frameStep = 0;
output = tf.signal.frame(input, frameLength, frameStep);
expect(output.shape).toEqual([0, 6]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), []]);
return [2 /*return*/];
}
});
}); });
it('Padding with default value', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, padEnd, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 3;
frameStep = 3;
padEnd = true;
output = tf.signal.frame(input, frameLength, frameStep, padEnd);
expect(output.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 4, 5, 0]]);
return [2 /*return*/];
}
});
}); });
it('Padding with the given value', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, padEnd, padValue, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 3;
frameStep = 3;
padEnd = true;
padValue = 100;
output = tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);
expect(output.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3, 4, 5, 100]]);
return [2 /*return*/];
}
});
}); });
it('Padding all remaining frames with step=1', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, padEnd, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 4;
frameStep = 1;
padEnd = true;
output = tf.signal.frame(input, frameLength, frameStep, padEnd);
expect(output.shape).toEqual([5, 4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1, 2, 3, 4,
2, 3, 4, 5,
3, 4, 5, 0,
4, 5, 0, 0,
5, 0, 0, 0
]]);
return [2 /*return*/];
}
});
}); });
it('Padding all remaining frames with step=1 and given pad-value', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, padEnd, padValue, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 4;
frameStep = 1;
padEnd = true;
padValue = 42;
output = tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);
expect(output.shape).toEqual([5, 4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1, 2, 3, 4,
2, 3, 4, 5,
3, 4, 5, 42,
4, 5, 42, 42,
5, 42, 42, 42
]]);
return [2 /*return*/];
}
});
}); });
it('Padding all remaining frames with step=2', function () { return __awaiter(_this, void 0, void 0, function () {
var input, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
output = tf.signal.frame(input, 4, 2, true);
expect(output.shape).toEqual([3, 4]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1, 2, 3, 4,
3, 4, 5, 0,
5, 0, 0, 0
]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('stft', jasmine_util_1.ALL_ENVS, function () {
it('3 length with hann window', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 3;
frameStep = 1;
output = tf.signal.stft(input, frameLength, frameStep);
expect(output.shape).toEqual([3, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1.0, 0.0, 0.0, -1.0, -1.0, 0.0,
1.0, 0.0, 0.0, -1.0, -1.0, 0.0,
1.0, 0.0, 0.0, -1.0, -1.0, 0.0,
]]);
return [2 /*return*/];
}
});
}); });
it('3 length with hann window (sequencial number)', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 3;
frameStep = 1;
output = tf.signal.stft(input, frameLength, frameStep);
expect(output.shape).toEqual([3, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
2.0, 0.0, 0.0, -2.0, -2.0, 0.0,
3.0, 0.0, 0.0, -3.0, -3.0, 0.0,
4.0, 0.0, 0.0, -4.0, -4.0, 0.0
]]);
return [2 /*return*/];
}
});
}); });
it('3 length, 2 step with hann window', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 3;
frameStep = 2;
output = tf.signal.stft(input, frameLength, frameStep);
expect(output.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1.0, 0.0, 0.0, -1.0, -1.0, 0.0,
1.0, 0.0, 0.0, -1.0, -1.0, 0.0
]]);
return [2 /*return*/];
}
});
}); });
it('3 fftLength, 5 frameLength, 2 step', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1, 1]);
frameLength = 5;
frameStep = 1;
fftLength = 3;
output = tf.signal.stft(input, frameLength, frameStep, fftLength);
expect(output.shape[0]).toEqual(2);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1.5, 0.0, -0.749999, 0.433,
1.5, 0.0, -0.749999, 0.433
]]);
return [2 /*return*/];
}
});
}); });
it('5 length with hann window', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 5;
frameStep = 1;
output = tf.signal.stft(input, frameLength, frameStep);
expect(output.shape).toEqual([1, 5]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(),
[2.0, 0.0, 0.0, -1.7071068, -1.0, 0.0, 0.0, 0.29289323, 0.0, 0.0]]);
return [2 /*return*/];
}
});
}); });
it('5 length with hann window (sequential)', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 5;
frameStep = 1;
output = tf.signal.stft(input, frameLength, frameStep);
expect(output.shape).toEqual([1, 5]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(),
[6.0, 0.0, -0.70710677, -5.1213202, -3.0, 1.0,
0.70710677, 0.87867975, 0.0, 0.0]]);
return [2 /*return*/];
}
});
}); });
it('3 length with hamming window', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 3;
frameStep = 1;
fftLength = 3;
output = tf.signal.stft(input, frameLength, frameStep, fftLength, function (length) { return tf.signal.hammingWindow(length); });
expect(output.shape).toEqual([3, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1.16, 0.0, -0.46, -0.79674333,
1.16, 0.0, -0.46, -0.79674333,
1.16, 0.0, -0.46, -0.79674333
]]);
return [2 /*return*/];
}
});
}); });
it('3 length, 2 step with hamming window', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 3;
frameStep = 2;
fftLength = 3;
output = tf.signal.stft(input, frameLength, frameStep, fftLength, function (length) { return tf.signal.hammingWindow(length); });
expect(output.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1.16, 0.0, -0.46, -0.79674333,
1.16, 0.0, -0.46, -0.79674333
]]);
return [2 /*return*/];
}
});
}); });
it('3 fftLength, 5 frameLength, 2 step with hamming window', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1, 1]);
frameLength = 5;
frameStep = 1;
fftLength = 3;
output = tf.signal.stft(input, frameLength, frameStep, fftLength, function (length) { return tf.signal.hammingWindow(length); });
expect(output.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
1.619999, 0.0, -0.69, 0.39837,
1.619999, 0.0, -0.69, 0.39837
]]);
return [2 /*return*/];
}
});
}); });
it('5 length with hann window (sequential)', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 2, 3, 4, 5]);
frameLength = 5;
frameStep = 1;
fftLength = 5;
output = tf.signal.stft(input, frameLength, frameStep, fftLength, function (length) { return tf.signal.hammingWindow(length); });
expect(output.shape).toEqual([1, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(),
[6.72, 0.0, -3.6371822, -1.1404576, 0.4771822, 0.39919350]]);
return [2 /*return*/];
}
});
}); });
it('3 length without window function', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, ident, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 3;
frameStep = 1;
fftLength = 3;
ident = function (length) { return tf.ones([length]).as1D(); };
output = tf.signal.stft(input, frameLength, frameStep, fftLength, ident);
expect(output.shape).toEqual([3, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
3.0, 0.0, 0.0, 0.0,
3.0, 0.0, 0.0, 0.0,
3.0, 0.0, 0.0, 0.0
]]);
return [2 /*return*/];
}
});
}); });
it('3 length, 2 step without window function', function () { return __awaiter(_this, void 0, void 0, function () {
var input, frameLength, frameStep, fftLength, ident, output, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
input = tf.tensor1d([1, 1, 1, 1, 1]);
frameLength = 3;
frameStep = 2;
fftLength = 3;
ident = function (length) { return tf.ones([length]).as1D(); };
output = tf.signal.stft(input, frameLength, frameStep, fftLength, ident);
expect(output.shape).toEqual([2, 2]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, output.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
3.0, 0.0, 0.0, 0.0,
3.0, 0.0, 0.0, 0.0
]]);
return [2 /*return*/];
}
});
}); });
});
//# sourceMappingURL=signal_ops_test.js.map