text: "use strict";
  /**
   * @license
   * Copyright 2017 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.
   * =============================================================================
   */
  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('div', test_util_1.ALL_ENVS, function () {
      it('same shape', function () {
          var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
          var c = tf.tensor2d([1, 2, 3, 4, 2, 5], [2, 3]);
          var r = tf.div(a, c);
          test_util_1.expectArraysClose(r, [1, 1, 1, 1, 2.5, 6 / 5]);
      });
      it('TensorLike', function () {
          var a = [0, 1, -2, -4, 4, -4];
          var b = [0.15, 0.2, 0.25, 0.5, 0.7, 1.2];
          var result = tf.div(a, b);
          expect(result.shape).toEqual([6]);
          test_util_1.expectArraysClose(result, [0, 5.0, -8.0, -8.0, 5.714285850524902, -3.3333332538604736]);
      });
      it('TensorLike chained', function () {
          var a = tf.tensor1d([0, 1, -2, -4, 4, -4]);
          var b = [0.15, 0.2, 0.25, 0.5, 0.7, 1.2];
          var result = a.div(b);
          expect(result.shape).toEqual(a.shape);
          test_util_1.expectArraysClose(result, [0, 5.0, -8.0, -8.0, 5.714285850524902, -3.3333332538604736]);
      });
      it('integer division implements floor divide', function () {
          var a = tf.tensor1d([-6, -6, -5, -4, -3, -3, 3, 3, 2], 'int32');
          var c = tf.tensor1d([-2, 2, 3, 2, -3, 3, 2, 3, 2], 'int32');
          var r = tf.div(a, c);
          expect(r.dtype).toEqual('int32');
          test_util_1.expectArraysClose(r, [3, -3, -2, -2, 1, -1, 1, 1, 1]);
      });
      it('integer division broadcasts', function () {
          var a = tf.tensor1d([-5, -4, 3, 2], 'int32');
          var c = tf.scalar(2, 'int32');
          var r = tf.div(a, c);
          expect(r.dtype).toEqual('int32');
          test_util_1.expectArraysClose(r, [-3, -2, 1, 1]);
      });
      it('propagates NaNs', function () {
          var a = tf.tensor2d([1, 2], [2, 1]);
          var c = tf.tensor2d([3, NaN], [2, 1]);
          var r = tf.div(a, c);
          test_util_1.expectArraysClose(r, [1 / 3, NaN]);
      });
      it('broadcasting same rank Tensors different shape', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([2, 3], [2, 1]);
          var result = tf.div(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [1 / 2, 1, -1, -4 / 3];
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcast 2D + 1D', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor1d([1, 2]);
          var result = tf.div(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [1, 1, -3, -2];
          test_util_1.expectArraysClose(result, expected);
      });
      it('upcasts when dtypes dont match', function () {
          var res = tf.div(tf.scalar(6, 'int32'), tf.scalar(3, 'float32'));
          expect(res.dtype).toBe('float32');
          test_util_1.expectArraysClose(res, [2]);
          res = tf.div(tf.scalar(6, 'int32'), tf.scalar(true, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [6]);
      });
      it('throws when passed tensors of different shapes', function () {
          var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
          var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
          expect(function () { return tf.div(a, b); }).toThrowError();
          expect(function () { return tf.div(b, a); }).toThrowError();
      });
      it('scalar divided by array', function () {
          var c = tf.scalar(2);
          var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
          var r = tf.div(c, a);
          test_util_1.expectArraysClose(r, [2 / 1, 2 / 2, 2 / 3, 2 / 4, 2 / 5, 2 / 6]);
      });
      it('scalar divided by array propagates NaNs', function () {
          var c = tf.scalar(NaN);
          var a = tf.tensor2d([1, 2, 3], [1, 3]);
          var r = tf.div(c, a);
          test_util_1.expectArraysEqual(r, [NaN, NaN, NaN]);
      });
      it('array divided by scalar', function () {
          var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
          var c = tf.scalar(2);
          var r = tf.div(a, c);
          test_util_1.expectArraysClose(r, [1 / 2, 2 / 2, 3 / 2, 4 / 2, 5 / 2, 6 / 2]);
      });
      it('array divided by scalar propagates NaNs', function () {
          var a = tf.tensor2d([1, 2, NaN], [1, 3]);
          var c = tf.scalar(2);
          var r = tf.div(a, c);
          test_util_1.expectArraysClose(r, [1 / 2, 2 / 2, NaN]);
      });
      it('gradient: Scalar', function () {
          var a = tf.scalar(5);
          var b = tf.scalar(2);
          var dy = tf.scalar(4);
          var before = tf.memory().numTensors;
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          var now = tf.memory().numTensors;
          expect(now).toBe(before + 2);
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [4 / 2]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-4 * 5 / (2 * 2)]);
      });
      it('gradient with clones', function () {
          var grads = tf.grads(function (a, b) { return tf.div(a.clone(), b.clone()).clone(); });
          var _a = grads([5, 2]), da = _a[0], db = _a[1];
          expect(da.shape).toEqual([]);
          expect(db.shape).toEqual([]);
          test_util_1.expectArraysClose(da, [1 / 2]);
          test_util_1.expectArraysClose(db, [-5 / 4]);
      });
      it('gradient: Tensor1D', function () {
          var a = tf.tensor1d([1, 2, 3]);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([1, 10, 20]);
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1 / 3, 10 / 4, 20 / 5]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1 * 1 / 9, -10 * 2 / 16, -20 * 3 / 25]);
      });
      it('gradient: Tensor1D with int32', function () {
          var a = tf.tensor1d([1, 2, 3], 'int32');
          var b = tf.tensor1d([3, 4, 5], 'int32');
          var dy = tf.tensor1d([1, 10, 20]);
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1 / 3, 10 / 4, 20 / 5]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1 * 1 / 9, -10 * 2 / 16, -20 * 3 / 25]);
      });
      it('gradient: 1d<int32> with 1d<bool> ', function () {
          var a = tf.tensor1d([true, false, true], 'bool');
          var b = tf.tensor1d([1, 2, 3], 'int32');
          var dy = tf.tensor1d([1, 19, 20]);
          var grads = tf.grads(function (a, b) { return tf.div(a.toInt(), b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1, 19 / 2, 20 / 3]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1 / 1, 0, -20 / 9]);
      });
      it('gradient: Tensor2D', function () {
          var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
          var b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
          var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1 / 1, 10 / 3, 15 / 4, 20 / 5]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1 * 3 / 1, -10 * 1 / 9, -15 * 2 / 16, -20 * 3 / 25]);
      });
      it('gradient: scalar / Tensor1D', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([6, 7, 8]);
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [6 / 3 + 7 / 4 + 8 / 5]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-6 * 2 / 9, -7 * 2 / 16, -8 * 2 / 25]);
      });
      it('gradient: Tensor2D / scalar', function () {
          var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
          var b = tf.scalar(2);
          var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [6 / 2, 7 / 2, 8 / 2, 9 / 2]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-6 * 2 / 4 + -7 * 3 / 4 + -8 * 4 / 4 + -9 * 5 / 4]);
      });
      it('gradient: Tensor2D / Tensor2D w/ broadcast', function () {
          var a = tf.tensor2d([3, 4], [2, 1]);
          var b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
          var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.div(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [6 / 2 + 7 / 3, 8 / 4 + 9 / 5]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-6 * 3 / 4, -7 * 3 / 9, -8 * 4 / 16, -9 * 4 / 25]);
      });
      it('throws when passed a as a non-tensor', function () {
          expect(function () { return tf.div({}, tf.scalar(1)); })
              .toThrowError(/Argument 'a' passed to 'div' must be a Tensor/);
      });
      it('throws when passed b as a non-tensor', function () {
          expect(function () { return tf.div(tf.scalar(1), {}); })
              .toThrowError(/Argument 'b' passed to 'div' must be a Tensor/);
      });
      it('accepts a tensor-like object', function () {
          var r = tf.div([[1, 2, 3], [4, 5, 6]], 2);
          expect(r.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(r, [1 / 2, 2 / 2, 3 / 2, 4 / 2, 5 / 2, 6 / 2]);
      });
  });
  jasmine_util_1.describeWithFlags('mul', test_util_1.ALL_ENVS, function () {
      it('strict same-shaped tensors', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
          var expected = [5, 6, -12, 28];
          var result = tf.mulStrict(a, b);
          expect(result.shape).toEqual([2, 2]);
          expect(result.dtype).toBe('float32');
          test_util_1.expectArraysClose(result, expected);
      });
      it('strict propagates NaNs', function () {
          var a = tf.tensor2d([1, 3, 4, 0], [2, 2]);
          var b = tf.tensor2d([NaN, 3, NaN, 3], [2, 2]);
          var result = tf.mulStrict(a, b);
          expect(result.dtype).toBe('float32');
          test_util_1.expectArraysClose(result, [NaN, 9, NaN, 0]);
      });
      it('strict throws when passed tensors of different shapes', function () {
          var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
          var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
          expect(function () { return tf.mulStrict(a, b); }).toThrowError();
          expect(function () { return tf.mulStrict(b, a); }).toThrowError();
      });
      it('strict throws when dtypes do not match', function () {
          var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3], 'float32');
          var b = tf.tensor2d([5, 3, 4, -7], [2, 2], 'int32');
          expect(function () { return tf.mulStrict(a, b); }).toThrowError();
          expect(function () { return tf.mulStrict(b, a); }).toThrowError();
      });
      it('strict int32 * int32', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2], 'int32');
          var b = tf.tensor2d([2, 1, 3, -4], [2, 2], 'int32');
          var res = tf.mulStrict(a, b);
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [2, 2, -9, 16]);
      });
      it('same-shaped tensors', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
          var expected = [5, 6, -12, 28];
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, expected);
      });
      it('TensorLike', function () {
          var a = [[1, 2], [-3, -4]];
          var b = [[5, 3], [4, -7]];
          var expected = [5, 6, -12, 28];
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, expected);
      });
      it('TensorLike chained', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = [[5, 3], [4, -7]];
          var expected = [5, 6, -12, 28];
          var result = a.mul(b);
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcasting tensors', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.scalar(2);
          var expected = [2, 4, -6, -8];
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcasting same rank Tensors different shape', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([2, 3], [2, 1]);
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [2, 4, -9, -12];
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcast 2D + 1D', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor1d([1, 2]);
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [1, 4, -3, -8];
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcast 5D + 2D', function () {
          var a = tf.range(1, 33).reshape([2, 2, 2, 2, 2]);
          var b = tf.tensor([2, 3], [2, 1]);
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2, 2, 2, 2]);
          var expected = [
              2, 4, 9, 12, 10, 12, 21, 24, 18, 20, 33, 36, 26, 28, 45, 48,
              34, 36, 57, 60, 42, 44, 69, 72, 50, 52, 81, 84, 58, 60, 93, 96
          ];
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcast 6D + 2D', function () {
          var a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
          var b = tf.tensor([2, 3], [2, 1]);
          var result = tf.mul(a, b);
          expect(result.shape).toEqual([2, 2, 2, 2, 2, 2]);
          var expected = [
              2, 4, 9, 12, 10, 12, 21, 24, 18, 20, 33, 36, 26,
              28, 45, 48, 34, 36, 57, 60, 42, 44, 69, 72, 50, 52,
              81, 84, 58, 60, 93, 96, 66, 68, 105, 108, 74, 76, 117,
              120, 82, 84, 129, 132, 90, 92, 141, 144, 98, 100, 153, 156,
              106, 108, 165, 168, 114, 116, 177, 180, 122, 124, 189, 192
          ];
          test_util_1.expectArraysClose(result, expected);
      });
      it('gradient: Scalar', function () {
          var a = tf.scalar(5);
          var b = tf.scalar(2);
          var dy = tf.scalar(4);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, b.mul(dy));
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, a.mul(dy));
      });
      it('gradient with clones', function () {
          var grads = tf.grads(function (a, b) { return tf.mul(a.clone(), b.clone()).clone(); });
          var _a = grads([4, 2]), da = _a[0], db = _a[1];
          expect(da.shape).toEqual([]);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, 2);
          expect(db.shape).toEqual([]);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, 4);
      });
      it('gradient: Tensor1D', function () {
          var a = tf.tensor1d([1, 2, 3]);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([1, 10, 20]);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [3 * 1, 4 * 10, 5 * 20]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [1 * 1, 2 * 10, 3 * 20]);
      });
      it('gradient: Tensor1D with dtype int32', function () {
          var a = tf.tensor1d([1, 2, 3], 'int32');
          var b = tf.tensor1d([3, 4, 5], 'int32');
          var dy = tf.tensor1d([1, 10, 20]);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [3 * 1, 4 * 10, 5 * 20]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [1 * 1, 2 * 10, 3 * 20]);
      });
      it('gradient: Tensor2D', function () {
          var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
          var b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
          var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1 * 1, 3 * 10, 4 * 15, 5 * 20]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [3 * 1, 1 * 10, 2 * 15, 3 * 20]);
      });
      it('gradient: scalar * Tensor1D', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([6, 7, 8]);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [3 * 6 + 4 * 7 + 5 * 8]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [2 * 6, 2 * 7, 2 * 8]);
      });
      it('gradient: Tensor2D * scalar', function () {
          var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
          var b = tf.scalar(2);
          var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [2 * 6, 2 * 7, 2 * 8, 2 * 9]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [2 * 6 + 3 * 7 + 4 * 8 + 5 * 9]);
      });
      it('gradient: Tensor2D * Tensor2D w/ broadcast', function () {
          var a = tf.tensor2d([3, 4], [2, 1]);
          var b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
          var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [2 * 6 + 3 * 7, 4 * 8 + 5 * 9]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [6 * 3, 7 * 3, 8 * 4, 9 * 4]);
      });
      it('complex number multiplication', function () {
          var real1 = tf.tensor1d([2]);
          var imag1 = tf.tensor1d([3]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([4]);
          var imag2 = tf.tensor1d([5]);
          var complex2 = tf.complex(real2, imag2);
          var result = complex1.mul(complex2);
          expect(result.dtype).toBe('complex64');
          expect(result.shape).toEqual([1]);
          test_util_1.expectArraysClose(result, [2 * 4 - 3 * 5, 2 * 5 + 3 * 4]);
      });
      it('complex number broadcasting multiplication', function () {
          var real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([4]);
          var imag2 = tf.tensor1d([5]);
          var complex2 = tf.complex(real2, imag2);
          var result = tf.mul(complex1, complex2);
          expect(result.dtype).toEqual('complex64');
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, [
              1 * 4 - 10 * 5, 1 * 5 + 10 * 4, 2 * 4 - 20 * 5, 2 * 5 + 20 * 4,
              -3 * 4 + 30 * 5, -3 * 5 + -30 * 4, -4 * 4 + 40 * 5, -4 * 5 + -40 * 4
          ]);
      });
      it('throws when passed a as a non-tensor', function () {
          expect(function () { return tf.mul({}, tf.scalar(1)); })
              .toThrowError(/Argument 'a' passed to 'mul' must be a Tensor/);
      });
      it('throws when passed b as a non-tensor', function () {
          expect(function () { return tf.mul(tf.scalar(1), {}); })
              .toThrowError(/Argument 'b' passed to 'mul' must be a Tensor/);
      });
      it('upcasts when dtypes dont match', function () {
          var res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(3, 'float32'));
          expect(res.dtype).toBe('float32');
          test_util_1.expectArraysClose(res, [6]);
          res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(true, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [2]);
          res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(false, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [0]);
      });
      it('accepts a tensor-like object', function () {
          var result = tf.mul([[1, 2], [-3, -4]], 2);
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, [2, 4, -6, -8]);
      });
  });
  jasmine_util_1.describeWithFlags('pow', test_util_1.ALL_ENVS, function () {
      it('same-shaped tensors', function () {
          var a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
          var b = tf.tensor2d([5, 3, 4, 5, 2, -3], [2, 3], 'int32');
          var expected = [1, -8, 81, 0, 49, 1];
          var result = tf.pow(a, b);
          expect(result.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(result, expected, 0.01);
      });
      it('TensorLike', function () {
          var a = [1, 2, 3];
          var exp = 2;
          var result = tf.pow(a, exp);
          expect(result.shape).toEqual([3]);
          expect(result.dtype).toBe('float32');
          test_util_1.expectArraysEqual(result, [1, 4, 9]);
      });
      it('TensorLike chained', function () {
          var a = tf.tensor1d([1, 2, 3]);
          var exp = 2;
          var result = a.pow(exp);
          expect(result.shape).toEqual([3]);
          expect(result.dtype).toBe('float32');
          test_util_1.expectArraysEqual(result, [1, 4, 9]);
      });
      it('int32^int32 returns int32', function () {
          var a = tf.tensor1d([1, 2, 3], 'int32');
          var exp = tf.scalar(2, 'int32');
          var result = tf.pow(a, exp);
          expect(result.shape).toEqual([3]);
          expect(result.dtype).toBe('int32');
          test_util_1.expectArraysEqual(result, [1, 4, 9]);
      });
      it('different-shaped tensors', function () {
          var a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
          var b = tf.scalar(2, 'int32');
          var expected = [1, 4, 9, 0, 49, 1];
          var result = tf.pow(a, b);
          expect(result.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(result, expected, 0.05);
      });
      it('propagates NaNs', function () {
          var a = tf.tensor2d([NaN, 3, NaN, 0], [2, 2]);
          var b = tf.tensor2d([1, 3, 2, 3], [2, 2], 'int32');
          var result = tf.pow(a, b);
          test_util_1.expectArraysClose(result, [NaN, 27, NaN, 0], 0.05);
      });
      it('handles non int32 exponent param', function () {
          var a = tf.tensor1d([2, 4]);
          var b = tf.tensor1d([.5, 1.2]);
          var result = tf.pow(a, b);
          var expected = [Math.pow(2, 0.5), Math.pow(4, 1.2)];
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcasting same rank Tensors different shape', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([2, 1], [2, 1], 'int32');
          var result = tf.pow(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [1, 4, -3, -4];
          test_util_1.expectArraysClose(result, expected);
      });
      it('broadcast 2D + 1D', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor1d([1, 2], 'int32');
          var result = tf.pow(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [1, 4, -3, 16];
          test_util_1.expectArraysClose(result, expected);
      });
      it('powStrict same-shaped tensors', function () {
          var a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
          var b = tf.tensor2d([5, 3, 4, 5, 2, -3], [2, 3], 'int32');
          var expected = [1, -8, 81, 0, 49, 1];
          var result = tf.powStrict(a, b);
          expect(result.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(result, expected, 0.01);
      });
      it('powStrict throws when passed tensors of different shapes', function () {
          var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
          var b = tf.tensor2d([5, 3, 4, -7], [2, 2], 'int32');
          expect(function () { return tf.powStrict(a, b); }).toThrowError();
      });
      it('powStrict handles non int32 exponent param', function () {
          var a = tf.tensor1d([2, 4]);
          var b = tf.tensor1d([.5, 1.2]);
          var result = tf.powStrict(a, b);
          var expected = [Math.pow(2, 0.5), Math.pow(4, 1.2)];
          test_util_1.expectArraysClose(result, expected);
      });
      it('gradients: Scalar ^ Scalar', function () {
          var a = tf.scalar(5);
          var b = tf.scalar(2, 'int32');
          var dy = tf.scalar(3);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [2 * 5 * 3]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [3 * Math.pow(5, 2) * Math.log(5)]);
      });
      it('gradient with clones', function () {
          var a = tf.scalar(5);
          var b = tf.scalar(2, 'int32');
          var grads = tf.grads(function (a, b) { return tf.pow(a.clone(), b.clone()).clone(); });
          var _a = grads([a, b]), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [2 * 5]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [Math.pow(5, 2) * Math.log(5)]);
      });
      it('gradients: x ^ 2 where x = 0', function () {
          var f = function (x) { return x.pow(tf.scalar(2)).asScalar(); };
          var g = tf.grad(f)(tf.scalar(0));
          test_util_1.expectArraysClose(g, [0]);
      });
      it('gradients: Scalar ^ Scalar fractional exponent', function () {
          var a = tf.scalar(4.0);
          var b = tf.scalar(1.5);
          var dy = tf.scalar(3.0);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1.5 * Math.pow(4, 0.5) * 3]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [3.0 * Math.pow(4, 1.5) * Math.log(4.0)]);
      });
      it('gradients: Tensor ^ Tensor', function () {
          var a = tf.tensor1d([-1, .5, 2]);
          var b = tf.tensor1d([3, 2, -1], 'int32');
          var dy = tf.tensor1d([1, 5, 10]);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [
              3 * Math.pow(-1, 2) * 1, 2 * Math.pow(.5, 1) * 5,
              -1 * Math.pow(2, -2) * 10
          ], 1e-1);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [
              0, 5 * Math.pow(.5, 2) * Math.log(.5), 10 * Math.pow(2, -1) * Math.log(2)
          ]);
      });
      it('gradient wrt exponent with negative base', function () {
          var a = tf.tensor1d([-1, -.5, -2.7]);
          var b = tf.tensor1d([3, 2, -1], 'int32');
          var dy = tf.tensor1d([1, 1, 1]);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), db = _a[1];
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [0, 0, 0]);
      });
      it('gradient: scalar / Tensor1D', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([6, 7, 8]);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [
              6 * 3 * Math.pow(2, 2) + 7 * 4 * Math.pow(2, 3) + 8 * 5 * Math.pow(2, 4)
          ]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [
              6 * Math.pow(2, 3) * Math.log(2), 7 * Math.pow(2, 4) * Math.log(2),
              8 * Math.pow(2, 5) * Math.log(2)
          ]);
      });
      it('gradient: Tensor2D / scalar', function () {
          var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
          var b = tf.scalar(2);
          var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [
              6 * 2 * Math.pow(2, 1), 7 * 2 * Math.pow(3, 1), 8 * 2 * Math.pow(4, 1),
              9 * 2 * Math.pow(5, 1)
          ]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [6 * Math.pow(2, 2) * Math.log(2) + 7 * Math.pow(3, 2) * Math.log(3) +
                  8 * Math.pow(4, 2) * Math.log(4) + 9 * Math.pow(5, 2) * Math.log(5)]);
      });
      it('gradient: Tensor2D / Tensor2D w/ broadcast', function () {
          var a = tf.tensor2d([3, 4], [2, 1]);
          var b = tf.tensor2d([[2, 3], [.4, .5]], [2, 2]);
          var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [
              6 * 2 * Math.pow(3, 1) + 7 * 3 * Math.pow(3, 2),
              8 * .4 * Math.pow(4, .4 - 1) + 9 * .5 * Math.pow(4, .5 - 1)
          ]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [
              6 * Math.pow(3, 2) * Math.log(3), 7 * Math.pow(3, 3) * Math.log(3),
              8 * Math.pow(4, .4) * Math.log(4), 9 * Math.pow(4, .5) * Math.log(4)
          ]);
      });
      it('throws when passed base as a non-tensor', function () {
          expect(function () { return tf.pow({}, tf.scalar(1)); })
              .toThrowError(/Argument 'base' passed to 'pow' must be a Tensor/);
      });
      it('throws when passed exp as a non-tensor', function () {
          expect(function () { return tf.pow(tf.scalar(1), {}); })
              .toThrowError(/Argument 'exp' passed to 'pow' must be a Tensor/);
      });
      it('accepts a tensor-like object', function () {
          var result = tf.pow([1, 2, 3], 2);
          expect(result.shape).toEqual([3]);
          expect(result.dtype).toBe('float32');
          test_util_1.expectArraysEqual(result, [1, 4, 9]);
      });
      it('negative base and whole exponent not NaN', function () {
          var a = tf.tensor1d([-2, -3, -4], 'float32');
          var b = tf.tensor1d([2, -3, 4], 'float32');
          var expected = [Math.pow(-2, 2), Math.pow(-3, -3), Math.pow(-4, 4)];
          var result = tf.pow(a, b);
          test_util_1.expectArraysClose(result, expected);
      });
      it('negative base and fract exponent NaN', function () {
          var a = tf.tensor1d([-2, -3, -4], 'float32');
          var b = tf.tensor1d([2.1, -3.01, 4.1], 'float32');
          var expected = [NaN, NaN, NaN];
          var result = tf.pow(a, b);
          test_util_1.expectArraysClose(result, expected);
      });
  });
  jasmine_util_1.describeWithFlags('add', test_util_1.ALL_ENVS, function () {
      it('c + A', function () {
          var c = tf.scalar(5);
          var a = tf.tensor1d([1, 2, 3]);
          var result = tf.add(c, a);
          test_util_1.expectArraysClose(result, [6, 7, 8]);
      });
      it('c + A propagates NaNs', function () {
          var c = tf.scalar(NaN);
          var a = tf.tensor1d([1, 2, 3]);
          var res = tf.add(c, a);
          test_util_1.expectArraysEqual(res, [NaN, NaN, NaN]);
      });
      it('A + B broadcasting same rank Tensors different shape', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([2, 3], [2, 1]);
          var result = tf.add(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [3, 4, 0, -1];
          test_util_1.expectArraysClose(result, expected);
      });
      it('A + B broadcast 2D + 1D', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor1d([1, 2]);
          var result = tf.add(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [2, 4, -2, -2];
          test_util_1.expectArraysClose(result, expected);
      });
      it('A + B', function () {
          var a = tf.tensor1d([2, 5, 1]);
          var b = tf.tensor1d([4, 2, -1]);
          var result = tf.add(a, b);
          var expected = [6, 7, 0];
          test_util_1.expectArraysClose(result, expected);
      });
      it('TensorLike', function () {
          var a = [2, 5, 1];
          var b = [4, 2, -1];
          var result = tf.add(a, b);
          var expected = [6, 7, 0];
          test_util_1.expectArraysClose(result, expected);
      });
      it('TensorLike chained', function () {
          var a = tf.tensor1d([2, 5, 1]);
          var b = [4, 2, -1];
          var result = a.add(b);
          var expected = [6, 7, 0];
          test_util_1.expectArraysClose(result, expected);
      });
      it('A + B propagates NaNs', function () {
          var a = tf.tensor1d([2, 5, NaN]);
          var b = tf.tensor1d([4, 2, -1]);
          var res = tf.add(a, b);
          test_util_1.expectArraysClose(res, [6, 7, NaN]);
      });
      it('A + B throws when passed tensors with different shape', function () {
          var a = tf.tensor1d([2, 5, 1, 5]);
          var b = tf.tensor1d([4, 2, -1]);
          expect(function () { return tf.add(a, b); }).toThrowError();
          expect(function () { return tf.add(b, a); }).toThrowError();
      });
      it('2D+scalar broadcast', function () {
          var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
          var b = tf.scalar(2);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(res, [3, 4, 5, 6, 7, 8]);
      });
      it('scalar+1D broadcast', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([6]);
          test_util_1.expectArraysClose(res, [3, 4, 5, 6, 7, 8]);
      });
      it('2D+2D broadcast each with 1 dim', function () {
          var a = tf.tensor2d([1, 2, 5], [1, 3]);
          var b = tf.tensor2d([7, 3], [2, 1]);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(res, [8, 9, 12, 4, 5, 8]);
      });
      it('2D+2D broadcast inner dim of b', function () {
          var a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
          var b = tf.tensor2d([7, 3], [2, 1]);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(res, [8, 9, 12, 7, 8, 9]);
      });
      it('3D+scalar', function () {
          var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
          var b = tf.scalar(-1);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([2, 3, 1]);
          test_util_1.expectArraysClose(res, [0, 1, 2, 3, 4, 5]);
      });
      it('6D+scalar', function () {
          var a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
          var b = tf.scalar(-1);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
          var expectedResult = [
              -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
              15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
              31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
              47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
          ];
          test_util_1.expectArraysClose(res, expectedResult);
      });
      it('6D+2D', function () {
          var a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
          var b = tf.tensor2d([11, 13, 17, 19], [2, 2]);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
          var expectedResult = [
              11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,
              27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,
              43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,
              59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82
          ];
          test_util_1.expectArraysClose(res, expectedResult);
      });
      it('add tensors with 0 in shape', function () {
          var a = tf.tensor1d([1]);
          var b = tf.tensor3d([], [0, 0, 5]);
          var res = tf.add(a, b);
          expect(res.shape).toEqual([0, 0, 5]);
          test_util_1.expectArraysEqual(res, []);
      });
      it('gradient: scalar + 1D broadcast', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([7, 8, 9]);
          var grads = tf.grads(function (a, b) { return tf.add(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [7 + 8 + 9]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [7, 8, 9]);
      });
      it('gradient with clones', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([7, 8, 9]);
          var grads = tf.grads(function (a, b) { return tf.add(a.clone(), b.clone()).clone(); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [7 + 8 + 9]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [7, 8, 9]);
      });
      it('gradient: 2D + 2D broadcast', function () {
          var a = tf.tensor2d([2, 3], [2, 1]);
          var b = tf.tensor2d([4, 5, 6, 7], [2, 2]);
          var dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.add(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [5 + 4, 3 + 2]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [5, 4, 3, 2]);
      });
      it('complex number addition', function () {
          var real1 = tf.tensor1d([1]);
          var imag1 = tf.tensor1d([2]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([3]);
          var imag2 = tf.tensor1d([4]);
          var complex2 = tf.complex(real2, imag2);
          var result = complex1.add(complex2);
          expect(result.dtype).toBe('complex64');
          expect(result.shape).toEqual([1]);
          test_util_1.expectArraysClose(result, [4, 6]);
      });
      it('complex number reshape and then addition', function () {
          var real1 = tf.tensor1d([1]);
          var imag1 = tf.tensor1d([2]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([3]);
          var imag2 = tf.tensor1d([4]);
          var complex2 = tf.complex(real2, imag2);
          var complex1Reshaped = complex1.reshape([1, 1, 1]);
          var complex2Reshaped = complex2.reshape([1, 1, 1]);
          var result = complex1Reshaped.add(complex2Reshaped);
          expect(result.dtype).toBe('complex64');
          expect(result.shape).toEqual([1, 1, 1]);
          test_util_1.expectArraysClose(result, [4, 6]);
      });
      it('complex number broadcasting addition', function () {
          var real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([4]);
          var imag2 = tf.tensor1d([5]);
          var complex2 = tf.complex(real2, imag2);
          var result = tf.add(complex1, complex2);
          expect(result.dtype).toEqual('complex64');
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, [1 + 4, 10 + 5, 2 + 4, 20 + 5, -3 + 4, -30 + 5, -4 + 4, -40 + 5]);
      });
      it('throws when passed a as a non-tensor', function () {
          expect(function () { return tf.add({}, tf.scalar(1)); })
              .toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);
      });
      it('throws when passed b as a non-tensor', function () {
          expect(function () { return tf.add(tf.scalar(1), {}); })
              .toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);
      });
      it('upcasts when dtypes dont match', function () {
          var res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
          expect(res.dtype).toBe('float32');
          test_util_1.expectArraysClose(res, [2]);
          res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [2]);
          res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [1]);
          res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));
          expect(res.dtype).toBe('complex64');
          test_util_1.expectArraysClose(res, [5, 7]);
          res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));
          expect(res.dtype).toBe('complex64');
          test_util_1.expectArraysClose(res, [5, 7]);
      });
      it('accepts a tensor-like object', function () {
          var result = tf.add(5, [1, 2, 3]);
          test_util_1.expectArraysClose(result, [6, 7, 8]);
      });
  });
  jasmine_util_1.describeWithFlags('addN', test_util_1.ALL_ENVS, function () {
      it('a single tensor', function () {
          var res = tf.addN([tf.tensor1d([1, 2, 3])]);
          test_util_1.expectArraysClose(res, [1, 2, 3]);
      });
      it('two tensors, int32', function () {
          var res = tf.addN([
              tf.tensor1d([1, 2, -1], 'int32'),
              tf.tensor1d([5, 3, 2], 'int32'),
          ]);
          test_util_1.expectArraysClose(res, [6, 5, 1]);
          expect(res.dtype).toBe('int32');
          expect(res.shape).toEqual([3]);
      });
      it('three tensors', function () {
          var res = tf.addN([
              tf.tensor1d([1, 2]),
              tf.tensor1d([5, 3]),
              tf.tensor1d([-5, -2]),
          ]);
          test_util_1.expectArraysClose(res, [1, 3]);
          expect(res.dtype).toBe('float32');
          expect(res.shape).toEqual([2]);
      });
      it('accepts a tensor-like object', function () {
          var res = tf.addN([[1, 2], [3, 4]]);
          test_util_1.expectArraysClose(res, [4, 6]);
          expect(res.dtype).toBe('float32');
          expect(res.shape).toEqual([2]);
      });
      it('list of numbers gets treated as a list of scalars', function () {
          var res = tf.addN([1, 2, 3, 4]);
          test_util_1.expectArraysClose(res, [10]);
          expect(res.dtype).toBe('float32');
          expect(res.shape).toEqual([]);
      });
      it('errors if list is empty', function () {
          expect(function () { return tf.addN([]); })
              .toThrowError(/Must pass at least one tensor to tf.addN\(\), but got 0/);
      });
      it('errors if argument is not an array', function () {
          // tslint:disable-next-line:no-any
          expect(function () { return tf.addN(tf.scalar(3)); })
              .toThrowError(/The argument passed to tf.addN\(\) must be a list of tensors/);
      });
      it('errors if arguments not of same dtype', function () {
          expect(function () { return tf.addN([tf.scalar(1, 'int32'), tf.scalar(2, 'float32')]); })
              .toThrowError(/All tensors passed to tf.addN\(\) must have the same dtype/);
      });
      it('errors if arguments not of same shape', function () {
          expect(function () { return tf.addN([tf.scalar(1), tf.tensor1d([2])]); })
              .toThrowError(/All tensors passed to tf.addN\(\) must have the same shape/);
      });
  });
  jasmine_util_1.describeWithFlags('sub', test_util_1.ALL_ENVS, function () {
      it('c - A', function () {
          var c = tf.scalar(5);
          var a = tf.tensor1d([7, 2, 3]);
          var result = tf.sub(c, a);
          test_util_1.expectArraysClose(result, [-2, 3, 2]);
      });
      it('A - c', function () {
          var a = tf.tensor1d([1, 2, -3]);
          var c = tf.scalar(5);
          var result = tf.sub(a, c);
          test_util_1.expectArraysClose(result, [-4, -3, -8]);
      });
      it('A - c propagates NaNs', function () {
          var a = tf.tensor1d([1, NaN, 3]);
          var c = tf.scalar(5);
          var res = tf.sub(a, c);
          test_util_1.expectArraysClose(res, [-4, NaN, -2]);
      });
      it('A - B', function () {
          var a = tf.tensor1d([2, 5, 1]);
          var b = tf.tensor1d([4, 2, -1]);
          var result = tf.sub(a, b);
          var expected = [-2, 3, 2];
          test_util_1.expectArraysClose(result, expected);
      });
      it('TensorLike', function () {
          var a = [2, 5, 1];
          var b = [4, 2, -1];
          var result = tf.sub(a, b);
          var expected = [-2, 3, 2];
          test_util_1.expectArraysClose(result, expected);
      });
      it('TensorLike chained', function () {
          var a = tf.tensor1d([2, 5, 1]);
          var b = [4, 2, -1];
          var result = a.sub(b);
          var expected = [-2, 3, 2];
          test_util_1.expectArraysClose(result, expected);
      });
      it('A - B propagates NaNs', function () {
          var a = tf.tensor1d([2, 5, 1]);
          var b = tf.tensor1d([4, NaN, -1]);
          var res = tf.sub(a, b);
          test_util_1.expectArraysClose(res, [-2, NaN, 2]);
      });
      it('A - B throws when passed tensors with different shape', function () {
          var a = tf.tensor1d([2, 5, 1, 5]);
          var b = tf.tensor1d([4, 2, -1]);
          expect(function () { return tf.sub(a, b); }).toThrowError();
          expect(function () { return tf.sub(b, a); }).toThrowError();
      });
      it('A - B broadcasting same rank Tensors different shape', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor2d([2, 3], [2, 1]);
          var result = tf.sub(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [-1, 0, -6, -7];
          test_util_1.expectArraysClose(result, expected);
      });
      it('A - B broadcast 2D + 1D', function () {
          var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var b = tf.tensor1d([1, 2]);
          var result = tf.sub(a, b);
          expect(result.shape).toEqual([2, 2]);
          var expected = [0, 0, -4, -6];
          test_util_1.expectArraysClose(result, expected);
      });
      it('2D-scalar broadcast', function () {
          var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
          var b = tf.scalar(2);
          var res = tf.sub(a, b);
          expect(res.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(res, [-1, 0, 1, 2, 3, 4]);
      });
      it('scalar-1D broadcast', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
          var res = tf.sub(a, b);
          expect(res.shape).toEqual([6]);
          test_util_1.expectArraysClose(res, [1, 0, -1, -2, -3, -4]);
      });
      it('2D-2D broadcast each with 1 dim', function () {
          var a = tf.tensor2d([1, 2, 5], [1, 3]);
          var b = tf.tensor2d([7, 3], [2, 1]);
          var res = tf.sub(a, b);
          expect(res.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(res, [-6, -5, -2, -2, -1, 2]);
      });
      it('2D-2D broadcast inner dim of b', function () {
          var a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
          var b = tf.tensor2d([7, 3], [2, 1]);
          var res = tf.sub(a, b);
          expect(res.shape).toEqual([2, 3]);
          test_util_1.expectArraysClose(res, [-6, -5, -2, 1, 2, 3]);
      });
      it('3D-scalar', function () {
          var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
          var b = tf.scalar(-1);
          var res = tf.sub(a, b);
          expect(res.shape).toEqual([2, 3, 1]);
          test_util_1.expectArraysClose(res, [2, 3, 4, 5, 6, 7]);
      });
      it('gradients: basic 1D arrays', function () {
          var a = tf.tensor1d([1, 2, 3]);
          var b = tf.tensor1d([3, 2, 1]);
          var dy = tf.tensor1d([1, 10, 20]);
          var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1, 10, 20]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1, -10, -20]);
      });
      it('gradient with clones', function () {
          var a = tf.tensor1d([1, 2, 3]);
          var b = tf.tensor1d([3, 2, 1]);
          var dy = tf.tensor1d([1, 10, 20]);
          var grads = tf.grads(function (a, b) { return tf.sub(a.clone(), b.clone()).clone(); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1, 10, 20]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1, -10, -20]);
      });
      it('gradients: basic 2D arrays', function () {
          var a = tf.tensor2d([0, 1, 2, 3], [2, 2]);
          var b = tf.tensor2d([3, 2, 1, 0], [2, 2]);
          var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [1, 10, 15, 20]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-1, -10, -15, -20]);
      });
      it('gradient: 1D - scalar broadcast', function () {
          var a = tf.tensor1d([3, 4, 5]);
          var b = tf.scalar(2);
          var dy = tf.tensor1d([7, 8, 9]);
          var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [7, 8, 9]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-7 - 8 - 9]);
      });
      it('gradient: scalar - 1D broadcast', function () {
          var a = tf.scalar(2);
          var b = tf.tensor1d([3, 4, 5]);
          var dy = tf.tensor1d([7, 8, 9]);
          var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [7 + 8 + 9]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-7, -8, -9]);
      });
      it('gradient: 2D - 2D broadcast', function () {
          var a = tf.tensor2d([4, 5, 6, 7], [2, 2]);
          var b = tf.tensor2d([2, 3], [2, 1]);
          var dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
          var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
          var _a = grads([a, b], dy), da = _a[0], db = _a[1];
          expect(da.shape).toEqual(a.shape);
          expect(da.dtype).toEqual('float32');
          test_util_1.expectArraysClose(da, [5, 4, 3, 2]);
          expect(db.shape).toEqual(b.shape);
          expect(db.dtype).toEqual('float32');
          test_util_1.expectArraysClose(db, [-5 - 4, -3 - 2]);
      });
      it('complex number subtraction', function () {
          var real1 = tf.tensor1d([3]);
          var imag1 = tf.tensor1d([5]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([1]);
          var imag2 = tf.tensor1d([0]);
          var complex2 = tf.complex(real2, imag2);
          var result = complex1.sub(complex2);
          expect(result.dtype).toBe('complex64');
          expect(result.shape).toEqual([1]);
          test_util_1.expectArraysClose(result, [2, 5]);
      });
      it('complex number broadcasting subtraction', function () {
          var real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
          var imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
          var complex1 = tf.complex(real1, imag1);
          var real2 = tf.tensor1d([4]);
          var imag2 = tf.tensor1d([5]);
          var complex2 = tf.complex(real2, imag2);
          var result = tf.sub(complex1, complex2);
          expect(result.dtype).toEqual('complex64');
          expect(result.shape).toEqual([2, 2]);
          test_util_1.expectArraysClose(result, [1 - 4, 10 - 5, 2 - 4, 20 - 5, -3 - 4, -30 - 5, -4 - 4, -40 - 5]);
      });
      it('throws when passed a as a non-tensor', function () {
          expect(function () { return tf.sub({}, tf.scalar(1)); })
              .toThrowError(/Argument 'a' passed to 'sub' must be a Tensor/);
      });
      it('throws when passed b as a non-tensor', function () {
          expect(function () { return tf.sub(tf.scalar(1), {}); })
              .toThrowError(/Argument 'b' passed to 'sub' must be a Tensor/);
      });
      it('upcasts when dtypes dont match', function () {
          var res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
          expect(res.dtype).toBe('float32');
          test_util_1.expectArraysClose(res, [0]);
          res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [0]);
          res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
          expect(res.dtype).toBe('int32');
          test_util_1.expectArraysClose(res, [1]);
          res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'float32'));
          expect(res.dtype).toBe('complex64');
          test_util_1.expectArraysClose(res, [3, 7]);
          res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'int32'));
          expect(res.dtype).toBe('complex64');
          test_util_1.expectArraysClose(res, [3, 7]);
      });
      it('accepts a tensor-like object', function () {
          var result = tf.sub(5, [7, 2, 3]);
          test_util_1.expectArraysClose(result, [-2, 3, 2]);
      });
  });
  //# sourceMappingURL=arithmetic_test.js.map